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UNIVERSIDADE TÉCNICA DE LISBOA
INSTITUTO SUPERIOR TÉCNICO
Diversity-Enhanced Genetic Algorithms for
Dynamic Optimization
CARLOS MIGUEL DA COSTA FERNANDES (Mestre)
Dissertação para obtenção do Grau de Doutor em Engenharia
Electrotécnica e de Computadores
Orientador: Doutor Agostinho Cláudio da Rosa
Júri
Presidente: Presidente do Conselho Científico do IST
Vogais: Doutor Rui Luís Vilela de Lima Mendes
Doutor Agostinho Cláudio da Rosa
Doutor Juan Julián Merelo Guervós
Doutor Fernando Miguel Pais da Graça Lobo
Doutor Nuno Cavaco Gomes Horta
Doutor Pedro Manuel Santos de Carvalho
Dezembro de 2009
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Título: Algoritmos Genéticos com Diversidade Genética para Optimização Dinâmica
Nome: Carlos Miguel da Costa Fernandes
Doutoramento em: Engenharia Electrotécnica e de Computadores
Orientador: Agostinho Cláudio da Rosa
Resumo
Diversas aplicações industriais têm componentes dinâmicas que conduzem a variações na
função objectivo, e os Algoritmos Genéticos (AGs), devido à adaptabilidade, surgem como
ferramentas apropriadas para resolver este tipo de problemas. A tese propõe dois novos
métodos evolutivos para optimização dinâmica. O primeiro está direccionado para a
recombinação e, com um mecanismo auto-regulado, evita cruzamentos entre soluções
semelhantes, mantendo, dessa forma, diversidade genética. O segundo é um novo operador
de mutação do qual emergem taxas variáveis auto-reguladas, com uma distribuição
adequada para optimização dinâmica. Propõe-se ainda um método híbrido eficaz que
combina as duas estratégias. O objectivo, e principal alegação da tese, é criar protocolos
inspirados em sistemas reais que melhoram o desempenho dos AGs sem aumentar a sua
complexidade, e sem informação a priori sobre o problema.
As propostas foram testadas numa vasta gama de problemas e demonstraram ser mais
eficazes do que outros AGs, nomeadamente quando as mudanças não são rápidas. O
algoritmo híbrido demonstrou ser particularmente eficaz, pois alarga a gama de aplicações
nas quais cada método, isolado, tem um bom desempenho. Tal como é proposto, os
algoritmos são robustos e não aumentam o espaço de parâmetros, cumprindo dessa forma
algumas exigências das aplicações reais.
Palavras-chave: Computação Evolutiva, Algoritmos Genéticos, Optimização Dinâmica,
Acasalamenteo Não-Aleatório, Criticalidade Auto-Organizada, Diversidade Genética.
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Abstract
Many industrial applications have dynamic components that lead to variations of the
fitness function and Genetic Algorithms (GAs) adaptiveness is an appropriate tool to solve
this type of problems. The thesis proposes two new evolutionary methods to tackle dynamic
problems. The first acts upon mating and avoids crossover between similar individuals, via
a self-regulated mechanism, thus preserving genetic diversity. The second is a new
mutation operator able to evolve self-regulated mutation rates with a particular distribution
that is suited for dynamic optimization. Finally, an efficient hybrid method that combines
both strategies is proposed. The objective and main claim is the possibility of designing
nature-inspired protocols for GAs that are efficient when evolving on dynamic
environments while preserving algorithms’ complexity and not requiring a priori
information about the problem.
The proposals are tested on a wide range of problems and are able to outperform
frequently other GAs, namely when the frequency of change is lower. The hybrid scheme
proves to be particularly effective since it broadened the range of dynamics in which each
method by itself excels. As projected, the proposed techniques are robust and do not
increase parameters’ set, thus fulfilling necessary conditions for real-world applications.
Keywords: Evolutionary Computation, Genetic Algorithms, Dynamic Optimization,
Genetic Diversity, Non-Random Mating, Self-Organized Criticality.
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Acknowledgments
I have a debt of gratitude to all of you who assisted me, encouraged me and guided me
during this long and hard (although exhilarating) project.
I am deeply grateful to all my colleagues, co-workers, and co-authors, both from Laseeb,
in Lisbon, and Geneura, in Granada, and also to Claudio Lima (also one of my co-authors)
from Universidade do Algarve, who introduced me to the fascinating new generation of
Evolutionary Algorithms. In particular, I would like to salute Juanlu, my colleague at
Geneura and my dear friend, who supported me in some the hardest periods of this work. I
also thank to my advisor Agostinho Rosa, and the thesis committee, Rui Vilela Mendes and
Pedro Carvalho, for their support and advice.
A special thank to J.J. Merelo, who welcomed me in Granada in his prolific and ―warm‖
laboratory. Another special thank to Jorge Calado; although we never discussed a single
issue of this thesis, it is of his sage advices I think often on those occasions when things
hardly make any sense.
To my Father and my Mother goes my deepest expression of gratitude.
Finally, this thesis is dedicated to Maria João Martins.
Carlos M. Fernandes
Granada, March 15, 2009
This work was supported by Ministério da Ciência e Tecnologia, Research Fellowship
SFRH/BD/18868/2004, partially supported by Fundação para a Ciência e a Tecnologia
(ISR/IST plurianual funding) through POS_Conhecimento Program that includes FEDER
funds.
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Table of Contents
Chapter 1 ............................................................................................................................................. 1
1.1 Objectives ............................................................................................................................ 1
1.2 Contributions ....................................................................................................................... 7
1.3 Thesis Outline ................................................................................................................... 10
Chapter 2 ........................................................................................................................................... 13
2.1 Introduction ....................................................................................................................... 13
2.2 Evolutionary Algorithms ................................................................................................... 14
2.3 Uncertainties in Optimization Problems ........................................................................... 18
2.4 Dynamic Optimization ...................................................................................................... 19
2.5 Evolutionary Algorithms for Dynamic Optimization ........................................................ 22
2.5.1 Estimation of Distribution Algorithms ...................................................................... 31
2.6 Swarm intelligence in Dynamic Environments ................................................................. 35
2.7 Dynamic Problems ............................................................................................................ 40
2.8 Dynamic Problems Generators .......................................................................................... 44
2.9 A Critical Note on the Experimental Research Methodology on Dynamic Optimization 47
2.10 Summary ........................................................................................................................... 49
Chapter 3 ........................................................................................................................................... 51
3.1 Introduction ....................................................................................................................... 51
3.2 Random and Non-Random Mating ................................................................................... 52
3.3 Non-Random Mating Evolutionary Algorithms ................................................................ 56
3.4 The Adaptive Dissortative Mating Genetic Algorithm ..................................................... 63
3.5 Summary ........................................................................................................................... 66
Chapter 4 ........................................................................................................................................... 67
4.1 Introduction ....................................................................................................................... 67
4.2 Functions ........................................................................................................................... 69
4.2.1 Methodology ............................................................................................................. 74
4.2.2 Results: Functions f1, f2 and f3 ................................................................................... 77
4.2.3 Results: Function f4 ................................................................................................... 81
4.2.4 Results: Knapsack Problem ....................................................................................... 85
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4.2.5 Results: Royal Road R1 ............................................................................................ 85
4.2.6 Results: Royal Road R4 ............................................................................................ 86
4.3 ADMGA Scalability with Trap Functions ........................................................................ 88
4.4 Genetic Diversity and Threshold Dynamics ...................................................................... 93
4.4.1 Genetic Diversity ....................................................................................................... 93
4.4.2 Threshold’s dynamics ................................................................................................ 96
4.5 Assortative Mating, Mutation Probability and Chromosome Codification ..................... 101
4.5.1 Assortative Mating and Mutation Probability ......................................................... 102
4.6 Extending ADMGA to Other Types of Codification ...................................................... 104
4.7 Summary ......................................................................................................................... 105
Chapter 5 ......................................................................................................................................... 107
5.1 Introduction ..................................................................................................................... 107
5.2 Results: Dynamic Trap Functions ................................................................................... 107
5.2.1 Standard GAs .......................................................................................................... 112
5.2.2 Random Immigrants GAs ........................................................................................ 122
5.2.3 Hypermutation Schemes .......................................................................................... 126
5.2.4 Assortative and Dissortative Mating GAs ............................................................... 129
5.2.5 Order-5 Dynamic Trap Problems ............................................................................ 133
5.3 Results: Dynamic Royal Road and Knapsack ................................................................. 135
5.3.1 Dynamic Royal Road .............................................................................................. 136
5.3.2 Dynamic 0-1 Knapsack ........................................................................................... 141
5.4 Summary ......................................................................................................................... 142
Chapter 6 ......................................................................................................................................... 145
6.1 Introduction ..................................................................................................................... 145
6.2 Self-Organized Criticality ............................................................................................... 146
6.2.1 Bak-Tang-Wisenfeld Sandpile Model ..................................................................... 150
6.2.2 The Bak-Sneppen Model ......................................................................................... 153
6.3 SOC in Evolutionary Computation ................................................................................. 154
6.4 The Sandpile Mutation .................................................................................................... 156
6.4.1 First version ............................................................................................................. 158
6.4.2 Second Version........................................................................................................ 163
6.5 Summary ......................................................................................................................... 166
Chapter 7 ......................................................................................................................................... 169
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7.1 Introduction ..................................................................................................................... 169
7.2 GGASM First Version: Results ......................................................................................... 170
7.3 GGASM (2): Results ......................................................................................................... 176
7.3.1 Comparing the Genetic Algorithms with Sand Pile Mutation ................................. 177
7.3.2 SORIGA and EIGA ................................................................................................. 179
7.4 Grain Rates and Mutation Rates ...................................................................................... 182
7.4.1 Grain Rate and Optimal Results .............................................................................. 183
7.4.2 Offline Mutation Rate ............................................................................................. 186
7.4.3 Online mutation rate ................................................................................................ 188
7.5 Summary ......................................................................................................................... 195
Chapter 8 ......................................................................................................................................... 197
8.1 Introduction ..................................................................................................................... 197
8.2 Dissortative Mating and Sand Pile Mutation................................................................... 197
8.3 Dynamic Royal Road Functions ...................................................................................... 201
8.4 Dynamic Knapsack Problems ......................................................................................... 203
8.5 Fast Dynamic Problems................................................................................................... 204
8.6 Summary ......................................................................................................................... 209
Chapter 9 ......................................................................................................................................... 213
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List of Figures
Figure 2.1: Pseudo-code of the Standard Genetic Algorithm (SGA). ................................................ 16
Figure 2.2: Pseudo-code of the Random Immigrants Genetic Algorithm (RIGA). ............................. 29
Figure 2.3: The Swarm Intelligence model by Fernandes et al. (2005a) evolving on a multimodal
function. .................................................................................................................................... 38
Figure 2.4: Dynamics proposed by Angeline (1992) as possible trajectories of the base function
over time along with their projection onto the (𝑥, 𝑦)-plane. Linear (left), circular (centre) and
random dynamics (right). Taken from (Angeline, 1992). .......................................................... 43
Figure 3.1: Pseudo-code of the Adaptive Dissortative Mating Genetic Algorithm (ADMGA). .......... 64
Figure 4.1: Three-dimensional plot of the sphere model. ................................................................ 69
Figure 4.2: Three-dimensional plot of the Ackley function. .............................................................. 70
Figure 4.3: Three-dimensional plot of the 𝑓4 function. .................................................................... 72
Figure 4.4: Function 𝑓3. AES values for different mutation probability values. Parameters: 𝑛 = 4;
𝑝𝑐 = 1.0; 𝑛 = 8. .................................................................................................................... 79
Figure 4.5: Function 𝑓1. Comparing ADMGA with two CHC-like algorithms. ................................... 80
Figure 4.6: The bisection method for determining the optimal population size of a GA. ................ 87
Figure 4.7: Generalized order-𝑘 trap function. ................................................................................. 90
Figure 4.8: Scalability with 𝑚−𝑘 trap functions (𝑘 = 2, 𝑘 = 3 and 𝑘 = 4). Optimal population
size and AES values for different problem size 𝑙 = 𝑘 × 𝑚. .................................................... 92
Figure 4.9: Scalability with 𝑚−𝑘 trap functions (𝑘 = 2, 𝑘 = 3 and 𝑘 = 4). Comparing ADMGA
with an elitist generational GA (GGA 2-e) and a steady-state GA (SSGA)................................. 92
Figure 4.10: Genetic diversity and best fitness. 𝑚−𝑘 trap function with 𝑘 = 4 and 𝑚 = 10.
Selectorecombinative GAs with 𝑝𝑐 = 1.0, 𝑛 = 400 and 2-elitism. SSGA 1 creates and
replaces 2 individuals per generation. SSGA 2 creates and replaces 𝑛2 = 200 individuals per
generation. ................................................................................................................................ 95
Figure 4.11: Genetic diversity and best fitness. Comparing SSGA 2 with nAMSSGA (top) and
pAMSSGA (bottom). 𝑚−𝑘 trap function with 𝑘 = 4 and 𝑚 = 10. Selectorecombinative GAs
with 𝑝𝑐 = 1.0, 𝑛 = 400 and 2-elitism. .................................................................................. 96
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Figure 4.12: Function 𝑓3. ADMGA’s threshold value after one generation (𝑡𝑠𝑡 = 1) measured as
the percentage 𝑙. Results averaged over 10 independent runs. .............................................. 98
Figure 4.13: ADMGA’s threshold value after one generation (𝑡𝑠𝑡 = 1) measured as a percentage of
𝑙. Random selection. Results averaged over 10 independent runs. ......................................... 98
Figure 4.14: Function 𝑓1. 𝑡𝑠 when ADMGA is run with different 𝑝𝑚 values. Parameters: 𝑛 = 4
and 𝑙 = 200. Uniform crossover. Results averaged over 10 independent runs. ................... 99
Figure 4.15: Function 𝑓1 and 𝑓2. 𝑡𝑠 values. Parameters: 𝑛 = 4, 𝑙 = 200 and 𝑝𝑚 = 0.007.
Uniform crossover. Results averaged over 25 independent runs. ......................................... 100
Figure 4.16: Function 𝑓4. 𝑡𝑠 values with different population size 𝑛. Parameters: 𝑙 = 40 and
𝑝 𝑚 = 0.02. Uniform crossover. Results averaged over 25 runs. ......................................... 101
Figure 4.17: Threshold (𝑡𝑠) value during ADMGA’s run on three 𝑚−𝑘 trap functions with the same
length 𝑙 = 𝑘 × 𝑚 .................................................................................................................. 102
Figure 5.1: Order-3 dynamic trap functions: ADMGA, GGA and SSGA averaged offline performance
when population size 𝑛 changes. ............................................................................................ 112
Figure 5.2: Order-3 dynamic trap functions: GGA’s offline performance on the twelve scenarios
with different population size n and 𝑝𝑚 = 1𝑙 ...................................................................... 113
Figure 5.3: Order-3 dynamic problems: GGA, SSGA and ADMGA averaged offline performance
with different pm. Population size: 𝑛 = 30. ........................................................................... 113
Figure 5.4: Order-4 dynamic problems: GGA’s performance on the twelve scenarios with 𝑝𝑚 = 1𝑙
and three population size 𝑛 values. ......................................................................................... 115
Figure 5.5: Order-4 dynamic problems: comparing GGA, SSGA and ADMGA performance with
population size 𝑛 = 30. ......................................................................................................... 116
Figure 5.6: Order-4 dynamic problems. Comparing GGA, SSGA and ADMGA performance with
population size 𝑛 = 60 and different 𝑝𝑚values. ................................................................... 116
Figure 5.7: Order-4 dynamic problems. Best-of-generation values measured throughout the run —
online performance — when 𝜌 = 0.05 (top row) and 𝜌 = 0.95 (bottom row); 𝜀 = 48,000.
GGA (𝑝𝑚 = 1𝑙) and ADMGA (𝑝𝑚 = 2𝑙) with 𝑛 = 30 (left column) and 𝑛 = 60 (right).
................................................................................................................................................. 119
Figure 5.8: “Slow” order-4 dynamic problems. 𝜀 = 480,000. Online performance when 𝜌 = 0.05
(left) and 𝜌 = 0.95 (right). GGA ( 𝑝𝑚 = 1𝑙) and ADMGA (𝑝𝑚 = 2𝑙). Population size:
𝑛 = 30. .................................................................................................................................. 120
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Figure 5.9: “Fast” order-4 dynamic trap problems. 𝜀 = 2,400. Online performance with different
severity values. GGA (𝑝𝑚 = 1𝑙) and ADMGA (𝑝𝑚 = 1𝑙). Population size: 𝑛 = 30. ........ 121
Figure 5.10: Order-3 (top row) and order-4 (bottom row) dynamic trap problems. Comparing
ADMGA, RIGA and SORIGA with 𝑛 = 30. RIGA and SORIGA’s parameter 𝑟𝑟 is set to 𝑟𝑟 = 4.
................................................................................................................................................. 123
Figure 5.11: Comparing the offline performance of ADMGA, GGA and EIGA with different
population size 𝑛 values. ......................................................................................................... 124
Figure 5.12: Order-4 dynamic problems: comparing ADMGA, HM 1 (𝑝𝑚 = 0.5) and HM 2
(𝑝𝑚 = 0.3) performance with population size: 𝑛 = 30. .................................................. 127
Figure 5.13: Order-4 dynamic problems. Online performance of GGA and Hypermutation (HM 1)
on low severity scenarios (𝜏 = 0.05). Population size: 𝑛 = 30. GGA and HM 1 with
𝑝𝑚 = 1𝑙. Hypermutation: 𝑝𝑚 = 0.5. ................................................................................ 129
Figure 5.14: Order-3 dynamic problems (𝑘 = 3, 𝑚 = 10). Comparing GGA, and GGA with
dissortative (nAMGGA) and assortative mating (pAMGGA). Population size: 𝑛 = 30. Pool
size: 𝑝 = 4 (AMGGAs). .......................................................................................................... 130
Figure 5.15: Order-4 dynamic problems. Effects of increasing pool size 𝑝 on nAMGGA’s
performance. 𝑛 = 30 (first row) and 𝑛 = 60 (second row)................................................ 131
Figure 5.16: Order-5 dynamic problems. ADMGA and nAMGGA (𝑝 = 4) offline performance.
Population size: 𝑛 = 60. ........................................................................................................ 133
Figure 5.17: Order-5 problem with 𝜀 = 48,000 and 𝜌 = 0.05. nAMGGA online performance with
different 𝑝𝑚values. Population size: 𝑛 = 60. ....................................................................... 134
Figure 5.18: Offline performance of ADMGA (𝑝 𝑚 = 1𝑙), nAMGGA with p = 4 (𝑝𝑚 = 1(8 × 𝑙))
and nAMGA with p = 30 (𝑝𝑚 = 1(16 × 𝑙)) on twelve order-5 dynamic scenarios. Population
size: 𝑛 = 1,000. ..................................................................................................................... 134
Figure 5.19: Dynamic royal road R1 problems: ADMGA, GGA and EIGA offline performance.
Population size n = 30. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. GGA:
𝑝𝑚 = 1𝑙; EIGA: 𝑝𝑚 = 2𝑙, 𝑝𝑚𝑖 = 2𝑙 and 𝑟𝑒𝑖 = 0.2. ........................................................ 136
Figure 5.20: Dynamic royal road R1 problems: ADMGA, GGA and EIGA offline performance on the
nine scenarios. Population size: 𝑛 = 60. ADMGA: 𝑝𝑚 = 1𝑙 when 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙
otherwise. GGA: 𝑝𝑚 = 1𝑙. EIGA: 𝑝𝑚 = 1𝑙, 𝑝𝑚𝑖 = 1𝑙 and 𝑟𝑒𝑖 = 0.2. ............................. 137
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Figure 5.21: Royal Road R4. ADMGA and nAMGA online performance. Population size: 𝑛 = 3,000.
𝜀 = 300,000. ADMGA with 𝑝𝑚 = 2𝑙. nAMGA with 𝑝𝑚 = 1(2 × 𝑙) and 𝑝 = 4. ............ 140
Figure 5.22: Dynamic knapsack problem. Offline performance of ADMGA and EIGA. ADMGA
with 𝑝𝑚 = 1𝑙 and EIGA with 𝑝𝑚 = 2𝑙 ................................................................................. 142
Figure 6.1: The Gutenberg-Richter law. The magnitude of the earthquakes and their frequency
show a power-law proportion. Taken from (Bak, 1996). ........................................................ 149
Figure 6.2: The Bak-Sneppen model. .............................................................................................. 154
Figure 6.3: A section of a sand pile attached to a population: genes 𝑙1, 𝑙2, 𝑙3 from chromosomes
𝑛1, 𝑛2, 𝑛3 . The height of the cell (𝑙2, 𝑛2) is 𝑧(𝑙2, 𝑛2) = 3. If one grain is dropped in that
site, an avalanche may occur. Then, four grains will topple to the cell’s von Neumann
neighbourhood and (𝑙2, 𝑛3) will also reach critical 𝑧 value. If conditions are favourable,
avalanche and mutations may proceed. ................................................................................. 160
Figure 6.4: The pseudo-code of the sand pile mutation as proposed by Fernandes et al. (2008a).
................................................................................................................................................. 161
Figure 6.5: Order-4 dynamic trap functions. GGASM (1) and EIGA offline performance on each
dynamic scenario. Population size: 𝑛 = 60. Uniform crossover with 𝑝𝑐 = 1.0. Tournament
selection. GGASM (1) with 𝑔𝑟 = (𝑛 × 𝑙)4 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)16 otherwise. EIGA
with 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. .......................................................... 164
Figure 6.6: The pseudo-code of the sand pile mutation (enhanced version). The main differences
are in the initialization procedure and in the normalized fitness. .......................................... 165
Figure 7.1: Comparing GGA and GGASM offline performance on Royal Road with 𝜀 = 24,000.
Population size: 𝑛 = 120. GGA: 𝑝 𝑚 = 1𝑙. GGASM: 𝑔𝑟 = 10 × 𝑙. ...................................... 174
Figure 7.2: Royal Road. GGASM mutation rate variation with 𝜀 = 1,200. Mutation rate is measured
every generation by comparing all the alleles in the population before and after 𝑔𝑟 grains are
dropped on sand pile............................................................................................................... 175
Figure 7.3: Order-3 dynamic problems: GGASM (1) and GGASM (2) performance with different
𝑔𝑟 and population size: 𝑛 = 30. ........................................................................................... 177
Figure 7.4: Order-3 dynamic trap problems. GGASM (1) and GGASM (2) offline performance.
Population size: 𝑛 = 30. GGASM (1): 𝑔𝑟 = (𝑛 × 𝑙)4 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)16
otherwise. GGASM (2): 𝑔𝑟 = (𝑛 × 𝑙)16 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)32 otherwise. ...... 178
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Figure 7.5: Order-4 dynamic trap problems: GGASM (1) and GGASM (2) offline performance on
twelve scenarios. Population size: 𝑛 = 60. GGASM (1): 𝑔𝑟 = (𝑛 × 𝑙)4 if 𝜀 = 2,400 and
𝑔𝑟 = (𝑛 × 𝑙)16 otherwise. GGSM (2): 𝑔𝑟 = (𝑛 × 𝑙)8 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)32
otherwise. ................................................................................................................................ 178
Figure 7.6: Order-3 dynamic trap problems: GGASM (2), EIGA and SORIGA offline performance.
Population size: n = 30. GGASM (2): 𝑔𝑟 = (𝑛 × 𝑙)16 when 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)32
otherwise. EIGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. SORIGA: 𝑝𝑚 = 1(8 × 𝑙)
................................................................................................................................................. 179
Figure 7.7: Order-4 dynamic trap problems: GGASM (2), EIGA and SORIGA offline performance.
Population size: 𝑛 = 60. GGASM (2): 𝑔𝑟 = (𝑛 × 𝑙)8 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)32
otherwise. EIGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. SORIGA: 𝑝𝑚 = 1(16 × 𝑙)
if 𝜀 = 2,400 and 𝑝𝑚 = 1(4 × 𝑙) otherwise. ........................................................................ 180
Figure 7.8: Order-4 dynamic problem. GGASM (2) and EIGA online performance on the order-4
scenario with 𝜀 = 48,000 and 𝜌 = 0.95. Population size: 𝑛 = 60. GGASM (2) with 𝑔𝑟 =
(𝑛 × 𝑙)32. EIGA with 𝑝𝑚 = 2𝑙. Parameters as in Figure 7.7. .............................................. 182
Figure 7.9: Order-3 dynamic trap functions. GGA and GGASM (2) offline performance with different
𝑝𝑚 and 𝑔𝑟 values. Population size: 𝑛 = 30. ......................................................................... 184
Figure 7.10: Grain rates plotted against the resulting offline mutation rates. ............................... 187
Figure 7.11: Order-4 dynamic trap problems. GGASM (2) online mutation rate. Population size:
𝑛 = 60. Grain rate: 𝑔𝑟 = (𝑛 × 𝑙)32. ε = 2,400. .............................................................. 190
Figure 7.12: Order-4 dynamic trap problems. GGASM (2) online mutation rate. Population size:
𝑛 = 60. Grain rate: 𝑔𝑟 = (𝑛 × 𝑙)32. ε = 24,000. ............................................................ 191
Figure 7.13: Order-4 dynamic problems. Logarithm of the mutation rates abundance plotted
against their values. ................................................................................................................ 193
Figure 7.14: Order-4 dynamic trap problems. GGASM (2) online mutation rate. Population size:
𝑛 = 60. Grain rate: 𝑔𝑟 = (𝑛 × 𝑙)32. Speed of change ε = 2,400 (top), speed of change:
ε = 24,000 (bottom, only the mutation rates of the first 400 generations are plotted). .... 194
Figure 8.1: Order-3 dynamic trap problems: GGASM, ADMGA and ADMGASM offline performance.
Population size: 𝑛 = 30. GGSM (2) : 𝑔𝑟 = (𝑛 × 𝑙)16 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)32
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otherwise. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. ADMGASM: 𝑔𝑟 = (𝑛 ×
𝑙)2 for every 𝜀. ........................................................................................................................ 198
Figure 8.2: Order-4 dynamic trap problems: GGASM (2), ADMGA and ADMGASM offline
performance. Population size: 𝑛 = 60. GGSM (2): 𝑔𝑟 = (𝑛 × 𝑙)8 if 𝜀 = 2,400 and
𝑔𝑟 = (𝑛 × 𝑙)32 otherwise. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise.
ADMGASM: 𝑔𝑟 = (𝑛 × 𝑙)4 for every 𝜀. ................................................................................... 199
Figure 8.3: Dynamic royal road 𝑅1. GGASM (2), ADMGA, GGA and EIGA offline performance.
Population size: 𝑛 = 60. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. GGA:
𝑝𝑚 = 1𝑙. EIGA: 𝑝𝑚 = 2𝑙. GGASM (2): 𝑔𝑟 = (𝑛 × 𝑙)32. ...................................................... 201
Figure 8.4: Dynamic royal road R1 problems. ADMGA, ADMGASM and GGASM (2) offline
performance. Population size: 𝑛 = 60. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙
otherwise. ADMGASM: 𝑔𝑟 = (𝑛 × 𝑙)16 if 𝜀 = 2,400 and 𝑔𝑟 = (𝑛 × 𝑙)8 otherwise. GGASM
(2): 𝑔𝑟 = (𝑛 × 𝑙)32 for every 𝜀 value. .................................................................................. 201
Figure 8.5: Knapsack dynamic problems. ADMGA, EIGA and GGASM (2) offline performance.
ADMGA: 𝑝𝑚 = 1𝑙. EIGA: 𝑝𝑚 = 2𝑙. GGASM: 𝑔𝑟 = (𝑛 × 𝑙)2. ................................................ 203
Figure 8.6: Knapsack dynamic problems. ADMGA, EIGA and ADMGASM offline performance.
ADMGA: 𝑝𝑚 = 1𝑙. EIGA: 𝑝𝑚 = 2𝑙. ADMGASM: 𝑔𝑟 = (𝑛 × 𝑙)2. .......................................... 204
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List of Tables
Table 4.1: Knapsack problem data. 100 items (randomly generated) with strongly correlated sets
of randomly generated data. .................................................................................................... 73
Table 4.2: Functions 𝑓1, 𝑓2 and 𝑓3. Best results attained by each algorithm. The results are
averaged over 100 runs. All configurations attained 100% success rates. .............................. 78
Table 4.3: Comparing the performance of the algorithms using paired two tailed t-tests with 198
degrees of freedom at a 0.05 level of significance. Symbol ≈ means that the algorithms are
statistically equivalent, that is, the null hypothesis — the datasets from which the offline
performance and the standard deviation are calculated are drawn from the same distribution
— is not rejected. Parameters as in table 4.2. .......................................................................... 79
Table 4.4: Function 𝑓4. Best success rates and corresponding AES values for several 𝑝𝑚 values.
Parameters: GGA: 𝑛 = 60; 𝑝𝑐 = 0.9; nAMGA: 𝑛 = 30; 𝑝𝑐 = 0.9: pAMGA: 𝑛 = 60;
𝑝𝑐 = 1.0; ADMGA: 𝑛 = 360. ................................................................................................. 81
Table 4.5: Function 𝑓4. CHC success rates and corresponding AES. ................................................. 84
Table 4.6: Knapsack problem. Best configurations. Final MBF values (averaged over 100 runs) after
100,000 evaluations. Percentage of runs (%) in which the solution with fitness 1853 is
attained. .................................................................................................................................... 84
Table 4.7: Royal road R1. Best configurations. All configurations attained 100% success rates. .... 86
Table 4.8: Royal road modified R4. Optimal population size, success rate, and number of
evaluations to reach a solution averaged over 100 runs (plus standard deviation). ............... 88
Table 4.9: ADMGA threshold values after the first generation (𝑡 = 1). Parameter values and
recombination operators as in the best configurations found in previous sections. ............... 97
Table 4.10 ADMGA, SAMGA and SAMXPGA results on four different functions. SR measures the
number of runs in which the global optimum is attained. ...................................................... 104
Table 5.1: Order-3 trap functions (𝑘 = 3, 𝑚 = 10). Offline performance. Population size 𝑛 =
30. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2400 and 𝑝𝑚 = 2𝑙 otherwise. GGA: 𝑝𝑚 = 1𝑙 for every 𝜀.
SSGA: 𝑝𝑚 = 2𝑙 for every 𝜀. .................................................................................................... 114
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Table 5.2: Order-4 trap problems (𝑘 = 4, 𝑚 = 10). Offline performance of the best
configuration of each GA. Population size: 𝑛 = 30. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and
𝑝𝑚 = 2𝑙 otherwise. GGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 or 𝜀 = 48,000 and 𝑝𝑚 = 2𝑙 if
𝜀 = 24,000. SSGA: 𝑝𝑚 = 2𝑙 if 𝜀 = 2,400 or 𝜀 = 24,000 and 𝑝𝑚 = 4𝑙 if 𝜀 = 48,000.
................................................................................................................................................. 117
Table 5.3: Order-4 trap problems (𝑘 = 4, 𝑚 = 10). Offline performance of the best
configuration of each GA. Population size: 𝑛 = 60. ADMGA: 𝑝𝑚 = 1(2 × 𝑙) if 𝜀 = 2,400
and pm = 2𝑙 otherwise. GGA: 𝑝𝑚 = 1𝑙 for every ε. SSGA: 𝑝𝑚 = 2𝑙 for every 𝜀. SSGA:
𝑝𝑚 = 2𝑙 if 𝜀 = 2,400 or 𝜀 = 2,400 and 𝑝𝑚 = 4𝑙 if 𝜀 = 48,000. ................................. 117
Table 5.4: Statistical comparison by paired two-tailed t-tests with 58 degrees of freedom at a 0.05
level of significance. The null hypothesis states that the datasets from which the offline
performance and the standard deviation are calculated are drawn from the same distribution.
The test result is shown as + sign when ADMGA is significantly better than GGA or SSGA, −
sign when ADMGA is significantly worst, and ≈ sign when the algorithms are statistically
equivalent (i.e, the null hypothesis is not rejected). Parameters as in Table 5.1 and Table 5.2.
................................................................................................................................................. 118
Table 5.5: Order-4 trap problems (𝑘 = 4, 𝑚 = 10). Mean best-of-generation values attained by
the best configuration of each algorithm. Population size 𝑛 = 30. ADMGA: 𝑝 𝑚 = 1𝑙 if
𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. GGA: 𝑝𝑚 = 1𝑙 for every 𝜀. SORIGA: 𝑝𝑚 = 1(16 × 𝑙)
if 𝜀 = 2,400 and 𝑝𝑚 = 1(4 × 𝑙) otherwise. EIGA: 𝑝 𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝 𝑚 = 2𝑙
otherwise. ................................................................................................................................ 125
Table 5.6: Order-3 and order-4 dynamic problems (𝑚 = 10). Statistical comparison of algorithms
by paired two-tailed 𝑡-tests with 58 degrees of freedom at a 0.05 level significance.
Population size: 𝑛 = 30. The 𝑡-test results are shown as + signs when ADMGA is significantly
better, − signs when ADMGA is significantly worst, and ≈ signs when the algorithms are
statistically equivalent. Parameters as in Table 5.5. ............................................................... 126
Table 5.7: Order-4 trap problems (𝑘 = 4, 𝑚 = 10). Offline performance. Population size 𝑛 =
30. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2400 and 𝑝𝑚 = 2𝑙 otherwise. HM 1 (𝑝𝑚 = 0.5) and HM 2
(𝑝𝑚 = 0.3) with 𝑝𝑚 = 1𝑙 for every 𝜀. ............................................................................... 128
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Table 5.8: Statistical comparison of algorithms by paired two-tailed t-tests with 58 degrees of
freedom at a 0.05 level significance. The t-test result is shown as + sign when ADMGA is
significantly better than HM 1 or HM 2, − sign when is significantly worst, and ≈ sign when the
algorithms are statistically equivalent. Population size: 𝑛 = 30. ADMGA: 𝑝𝑚 = 1𝑙 if
𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. HM 1: 𝑝𝑚 = 1𝑙 and 𝑝𝑚 = 0.5. HM 2: 𝑝𝑚 = 1𝑙 and
𝑝𝑚 = 0.3. ............................................................................................................................. 128
Table 5.9: Order-3 (𝑘 = 3) and order-4 (𝑘 = 4) dynamic trap problems (𝑚 = 10). Statistical
comparison by paired two-tailed t-tests with 58 degrees of freedom at a 0.05 level
significance. The 𝑡-test results are shown as + sign when ADMGA is significantly better, − sign
when ADMGA is significantly worst, and ≈ sign when the algorithms are statistically
equivalent. ADMGA: 𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise. nAMGGA: 𝑝𝑚 = 1𝑙 if
𝑛 = 2, 𝑝𝑚 = 1(2 × 𝑙) if 𝑛 = 4 and 𝑝𝑚 = 1(4 × 𝑙) if 𝑛 = 8. ........................................ 132
Table 5.10: Order-5 dynamic trap problems (𝑘 = 5, 𝑚 = 10). Statistical t-tests. The ≈ sign means
that the algorithms are statistically equivalent. ADMGA with 𝑝𝑚 = 2𝑙. nAMGGA (𝑛 = 4)
with pm = 1(8 × 𝑙). Parameters as in Figure 5.18. .................................................................. 135
Table 5.11: Royal road R1 dynamic problems. Offline performance. Parameters as in Figure 5.19
and Figure 5.20. ....................................................................................................................... 137
Table 5.12: Royal road R1 dynamic problems. Statistical comparison of algorithms by paired two-
tailed t-tests with 58 degrees of freedom at a 0.05 level significance. The t-test result
regarding ADMGA and other GAs is shown as + sign when ADMGA is significantly better, −
sign when ADMGA is significantly worst, and ≈ sign when the algorithms are statistically
equivalent. Parameters as in Figure 5.19 and Figure 5.20. ..................................................... 138
Table 5.13: Royal road R1 dynamic problems. Offline performance. Population size n = 30. ADMGA:
𝑝𝑚 = 1𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 2𝑙 otherwise.; nAMGA with 𝑝 𝑚 = 1(2 × 𝑙) and 𝑝 = 4.
................................................................................................................................................. 139
Table 5.14: Royal road modified 𝑅4 dynamic problems. Mean best-of-generation values attained
by the best configuration of ADMGA and nAMGA (𝑛 = 4). The last row shows the best
algorithms in each scenario (after t-tests). The ≈ symbol means that the results are
statistically equivalent. ADMGA: 𝑝𝑚 = 2𝑙; nAMGA: 𝑝𝑚 = 1(2 × 𝑙) ................................... 139
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Table 5.15: Royal road modified R4 dynamic problems. Offline performance of ADMGA and
Hypermutation (HM). ADMGA: 𝑝𝑚 = 2𝑙; Hypermutation: 𝑝𝑚 = 1𝑙 and 𝑝𝑚 = 0.5 ....... 141
Table 5.16: Dynamic 0-1 knapsack problem. Offline performance of the best configuration of each
GA. ADMGA with 𝑝𝑚 = 1𝑙. GGA: 𝑝𝑚 = 2𝑙 if 𝜀 = 2,400 and 𝑝𝑚 = 1𝑙 otherwise. nAMGA
(𝑝 = 4): 𝑝𝑚 = 1(2 × 𝑙) if 𝜀 = 2,400 and 𝑝𝑚 = 1(4 × 𝑙) otherwise. EIGA: 𝑝𝑚 = 2𝑙. .. 141
Table 7.1: Royal Road R1 dynamic problems. Population size: 𝑛 = 120. 2-point crossover.
Crossover probability: 𝑝𝑐 = 0.7. Tournament selection (𝑘𝑡𝑠 = 0.9). GGASM: 𝑔𝑟 = 10 × 𝑙.
RIGA 1: 𝑝𝑚 = 1𝑙; 𝑟𝑟 = 3. RIGA 1: 𝑝𝑚 = 1𝑙; 𝑟𝑟 = 12. GGA: 𝑝𝑚 = 1𝑙. ............................. 172
Table 7.2: Deceptive 1 dynamic problems. Population size: 𝑛 = 120. 2-point crossover. Crossover
probability: 𝑝𝑐 = 0.7. Tournament selection (𝑘𝑡𝑠 = 0.9). GGASM: 𝑔𝑟 = 50 × 𝑙. RIGA 1:
𝑝𝑚 = 1𝑙; 𝑟𝑟 = 3. RIGA 1: 𝑝𝑚 = 1𝑙; 𝑟𝑟 = 12. GGA: 𝑝𝑚 = 1𝑙. ......................................... 172
Table 7.3: Deceptive 2 dynamic problems. Population size: 𝑛 = 120. 2-point crossover. Crossover
probability: 𝑝𝑐 = 0.7. Tournament selection (𝑘𝑡𝑠 = 0.9). GGASM: 𝑔𝑟 = 50 × 𝑙. RIGA 1:
𝑝𝑚 = 1𝑙; 𝑟𝑟 = 3. RIGA 1: 𝑝𝑚 = 1𝑙; 𝑟𝑟 = 12. GGA: 𝑝𝑚 = 1𝑙. ......................................... 172
Table 7.4: Royal Road R1 dynamic problems. Statistical comparison of algorithms by paired two-
tailed t-tests with 58 degrees of freedom at a 0.05 level of significance. The t-test result is
shown as + sign when ADMGA is significantly better than GGA or SSGA, − sign when ADMGA
is significantly worst, and ≈ sign when the algorithms are statistically equivalent. Parameter
settings as in Table 7.1. ........................................................................................................... 173
Table 7.5: Deceptive 1 dynamic problems. Statistical comparison of algorithms by paired two-
tailed t-tests with 58 degrees of freedom at a 0.05 level of significance. Parameter settings as
in Table 7.2. ............................................................................................................................. 173
Table 7.6: Deceptive 2 dynamic problems. Statistical comparison of algorithms by paired two-
tailed t-tests with 58 degrees of freedom at a 0.05 level of significance Parameter settings as
in Table 7.3. ............................................................................................................................. 173
Table 7.7: Comparing GGASM and SORIGA. Population size: 𝑛 = 120. 𝑝𝑚 = 0.01; 𝑝𝑐 = 0.7;
SORIGA: 𝑟𝑟 = 3; GGASM: 𝑔𝑟 = 10 × 𝑙 (Royal Road) and 𝑔𝑟 = 50 × 𝑙 (Deceptive 1 and 2).176
Table 7.8: Order-3 trap problems (𝑘 = 3, 𝑚 = 10). GGASM (2), GGA, SORIGA and EIGA offline
performance. Population size: 𝑛 = 30. Parameters as in Figure 7.6 (and GGA with 𝑝𝑚 = 1𝑙).
................................................................................................................................................. 180
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Table 7.9: Order-4 trap problems (𝑘 = 4, 𝑚 = 10). GGASM (2), GGA, SORIGA and EIGA offline
performance. Population size: 𝑛 = 30. Parameters as in Figure 7.7 (and GGA with 𝑝𝑚 = 1𝑙).
................................................................................................................................................. 181
Table 7.10: Order-3 and order-4 dynamic problems (𝑚 = 10). Statistical comparison of
algorithms by paired two-tailed t-tests with 58 degrees of freedom at a 0.05 level
significance. The t-test results are shown as + signs when GGASM (2) is significantly better, −
signs when GGASM (2) is significantly worst, and ≈ signs when the algorithms are statistically
equivalent. Parameters as in Figure 7.6 and Figure 7.7. ......................................................... 181
Table 7.11: Optimal GGASM (2) grain rates for each type of problem and type of scenario (𝑛 is the
population size and 𝑙 is the chromosome length). .................................................................. 183
Table 7.12: Equation 7.1 and equation 7.2 computed for each problem and type of scenario. .... 185
Table 7.13: Order-4 dynamic trap problems. Offline mutation rate............................................... 186
Table 7.14: Knapsack dynamic problems. Offline mutation rate. ................................................... 187
Table 7.15: Mutation rate median values (averaged over 30 independent runs). ......................... 189
Table 8.1: Order-3 and order-4 trap problems (𝑚 = 10). ADMGA, GGASM (2) and ADMGASM offline
performance. Population size: 𝑛 = 30. Parameters as in Figure 8.2. ................................... 200
Table 8.2: Order-3 and order-4 dynamic problems (𝑚 = 10). Population size: 𝑛 = 30 (order-3)
and 𝑛 = 60 (order-4). Statistical comparison of algorithms by paired two-tailed t-tests with
58 degrees of freedom at a 0.05 level significance. The t-test results regarding are shown as +
signs when ADMGASM is significantly better, − signs when is significantly worst, and ≈ signs
when the algorithms are statistically equivalent. Parameters as in Figure 8.1 and Figure 8.2.
................................................................................................................................................. 200
Table 8.3: Royal road R1 dynamic problems. Numerical results. Parameters as in Figure 8.3
(SORIGA with 𝑝𝑚 = 1(8 × 𝑙))................................................................................................ 202
Table 8.4: Royal road R1 dynamic problems. Population size 𝑛 = 60. Statistical comparison of the
GGASM (2) by paired two-tailed t-tests with 58 degrees of freedom at a 0.05 level
significance. The t-test results are shown as + signs when GGASM is significantly better, − signs
when GGASM is significantly worst, and ≈ signs when the algorithms are statistically
equivalent. Parameters as in Figure 8.3 (SORIGA with 𝑝𝑚 = 1(8 × 𝑙)). ............................... 202
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Table 8.5: Royal road R1 dynamic problems. Population size 𝑛 = 60. Statistical comparison of
ADMGASM, ADMGA and GGASM (2) by paired two-tailed t-tests with 58 degrees of freedom at
a 0.05 level significance. The t-test results are shown as + signs when ADMGASM is
significantly better, − signs when ADMGASM is significantly worst, and ≈ signs when the
algorithms are statistically equivalent. Parameters as in Figure 8.4....................................... 203
Table 8.5: Order-3 dynamic trap functions (𝑘 = 3; 𝑚 = 10). Population size: 𝑛 = 30. The t-test
results are shown as + signs when Alg(orithm) 1 is significantly better than Alg(orithm) 2, −
signs when algorithm 1 is significantly worst, and ≈ signs when the algorithms are statistically
equivalent. ............................................................................................................................... 206
Table 8.6: Order-4 dynamic trap functions (𝑘 = 4; 𝑚 = 10). Population size: 𝑛 = 60. The t-test
results are shown as + signs when algorithm 1 is significantly better than algorithm 2, − signs
when algorithm 1 is significantly worst, and ≈ signs when the algorithms are statistically
equivalent. ............................................................................................................................... 207
Table 8.7: Order-4 dynamic trap functions (𝑘 = 4; 𝑚 = 10). Population size: 𝑛 = 60. Statistical
tests. The t-test results are shown as + signs when algorithm 1 is significantly better than
algorithm 2, − signs when algorithm 1 is significantly worst, and ≈ signs when the algorithms
are statistically equivalent....................................................................................................... 209
Table 8.8: Order-4 dynamic trap functions (𝑘 = 4; 𝑚 = 10). Population size: 𝑛 = 60. Statistical
tests. The t-test results are shown as + signs when algorithm 1 is significantly better than
algorithm 2, − signs when algorithm 1 is significantly worst, and ≈ signs when the algorithms
are statistically equivalent....................................................................................................... 210
Table 8.9: Order-4 dynamic trap functions (𝑘 = 4; 𝑚 = 10). Population size: 𝑛 = 60. Results of
the t-tests comparing the performance of EIGA and GGA. The t-test results are shown as +
signs when algorithm 1 is significantly better than algorithm 2, − signs when algorithm 1 is
significantly worst, and ≈ signs when the algorithms are statistically equivalent. ................. 211
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List of Abbreviations
ACO: Ant Colony Optimization
ADMGA: Adaptive Dissortative Mating Genetic Algorithm
ADMGASM: Adaptive Dissortative Mating Genetic Algorithm with sand pile mutation
AMGA: Assortative Mating Genetic Algorithm
ECGA: Extended Compact Genetic Algorithm
EDA: Estimation of Distribution Algorithm
EIGA: Elitism-based Immigrants Genetic Algorithm
GA: Genetic Algorithm
GASM: Genetic Algorithm with sand pile mutation
GGA: Generational Genetic Algorithm
GGASM: Generational Genetic Algorithm with sand pile mutation
nAMGA: negative Assortative Mating Genetic Algorithm
pAMGA: positive Assortative Mating Genetic Algorithm
PBIL: Population Based Incremental Learning
PSO: Particle Swarm Optimization
RIGA: Random Immigrants Genetic Algorithm
SOC: Self-Organized Criticality
SORIGA: Self-Organized Random Immigrants Genetic Algorithm
SRP-EA: Self-Regulated Population size Evolutionary Algorithm
SSGA: Steady-State Genetic Algorithm
UMDA: Univariate Marginal Distribution Algorithm
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Sisyphus was condemned by the gods to forever roll a huge
stone up a mountain, only to see it fall back to the bottom
each time he reached the summit.
Samuel Florman, in The Existential Pleasures of Engineering
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1
Chapter 1
Introduction
1.1 Objectives
In recent years, dynamic optimization — i.e., optimization of non-stationary
functions — became one of the major themes of research on Evolutionary Computation
(Holland, 1975; Goldberg, 1989a; Bäck, 1996). Since dynamic components are, along
with non-linear constraints and multiple objectives, one of the properties that frequently
appear in real-world problems, and because for a long time Evolutionary Computation
has entered the realm of industrial applications — namely, due to its efficiency on non-
linearity and multiobjectives —, it was expected that, sooner or later, this field would
raise a growing interest amongst the community. The interest in the subject, though, is
not recent, and many studies have been published since the beginning of the
investigations on evolutionary systems for optimization purposes, in the 1960s. In fact,
and as referred by Jin and Branke1 in their survey on evolutionary optimization in
uncertain environments (Jin & Branke, 2005), the earliest application of Evolutionary
Computation to dynamic environments dates back to 1966, and its description appears
in the seminal book by Fogel2, Owens and Walsh, Artificial Intelligence through
Simulated Evolution (Fogel et al. 1966).
1 Jurgen Branke is one of the most prominent researchers in evolutionary optimization, author and co-author of many
papers on the subject, which will be addressed throughout this thesis. He published the book Evolutionary
Optimization in Dynamic Environments (Branke, 2002) in 2002, after his homonymous thesis in 2000 (Branke,
2000). 2 Lawrence Fogel (1928-2007) was one of Evolutionary Computation’s founding fathers, and the author of the first
dissertation in the field (Fogel, 1964).
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2
However, this line of investigation only fully started after Goldberg and Smith’s
paper (Goldberg & Smith, 1987) on diploid Genetic Algorithms (GAs) (Goldberg,
1989a) for dynamic optimization problems, published, in 1987. A few years later, two
important and widely cited papers were published, one by Cobb, proposing the well-
known Hypermutation scheme (Cobb, 1990) and another one by Grefenstette,
proposing the Random Immigrants Genetic Algorithm (Grefenstette, 1992). In a way,
each one of these approaches is a kind of main paradigm of two of the four categories
defined by Branke to classify evolutionary techniques for dynamic optimization
(Branke, 1999): reaction to changes (hypermutation), diversity maintenance (random
immigrants), memory schemes and multi-population approaches — see Chapter 2 for a
detailed description of Jin and Branke’s categories and of some of the previously
proposed Evolutionary Algorithms for dynamic optimization. Since Cobb and
Grefenstette’s papers, and especially after Branke’s research on this issue, the
investigations on Evolutionary Algorithms for dynamic optimization attracted a vast
number of scientists and the publications on the subject experienced a consistent
growth, on both international journals and peer-reviewed conference proceedings3.
Nowadays, these increasing research efforts are being mainly directed towards
diversity maintenance and memory schemes. This is probably because many multi-
population schemes may be classified within one of the remaining categories (and some
of them are really hard to distinguish from memory schemes), and because evolutionary
schemes that react to changes can only be applied when it is easy to detect those same
changes — and, in addition, their efficiency is strongly dependent on the intensity of
the changes. Moreover, ―pure‖ multi-population schemes usually require complex
updating and migration strategies that makes it difficult to tune and implement the
algorithms.
On the other hand, diversity maintenance techniques — which in general do not
require any knowledge about the problem and its dynamics —, may slow down the
3 For instance, in 2005, the Journal of Soft Computing released a special issue on dynamic optimization, and, in
2006, IEEE published a Transactions on Evolutionary Computation special issue on Evolutionary Computation in
the presence of uncertainty.
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3
convergence of the algorithm during the stationary periods, a characteristic that may
harm the performance when the consecutive changes in the fitness function are
separated by short periods of time. Finally, memory schemes may be very effective, but
their utility is believed to be restricted to a certain type of dynamics.
Each type of strategy has its advantages and drawbacks. However, diversity
maintenance algorithms, due to the fact that they usually do not (necessarily) rely on a
complex parameter setting and neither on any particular knowledge about the function
and the dynamics, may be regarded as the most robust evolutionary approach to
dynamic optimization (even though other strategies may be better when tackling
dynamic problems from which some information is available). For these reasons, the
conclusions about the algorithms’ spectrum of application are more reliable when
investigating Evolutionary Algorithms that act upon diversity in order to tackle
dynamic problems, since they are not designed to match any particular characteristic of
those problems.
This thesis is focused on this type of approaches and argues that it is possible — by
searching for inspiration in natural systems — to develop new techniques and improve
not only standard GAs on dynamic optimization problems, but also other diversity
maintenance strategies that are being proposed by the scientific community. In addition
(and this is an important issue), it is possible to build those schemes by relying on a
self-adjustable behaviour, without increasing the algorithms’ complexity — it is hard to
evaluate the pay-off of using a novel technique if the parameter space grows or if it
narrows the region of the parameter space in which the algorithm performs well.
Finally, it is hoped that this work also sheds some light on the behaviour of traditional
GAs on dynamic environments, namely on how their performance reacts to different
parameter settings. It is shown, for instance, that standard GAs may work better than it
is believed, when compared to state-of-the-art proposals, if the parameters are properly
tuned. Moreover, two of the typical Evolutionary Algorithms’ parameters —
population size and mutation probability — are shown to deeply affect the algorithms’
performance. Only after understanding the full extent of Evolutionary Algorithms’
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efficiency on dynamic environments, it is possible to design alternative schemes that
can improve their performance on a significant number of problems and dynamics.
The thesis restricts the investigation to GAs4 —although some features of the
proposed schemes are easily extended to other Evolutionary Algorithms (Bäck, 1996)
— and proposes two bio-inspired techniques that enhance their performance on a wide
range of problems, and a hybrid scheme that mixes both strategies and further enhances
the performance. As stated above, the research has been mainly focused on diversity
maintenance techniques, due to their broader scope of applications and because these
schemes are closer to the definition of ―black-box dynamic optimization5‖. The
fundamental challenge is to deal with changing environments without information
regarding the changes — although it is assumed that the number of environments or the
period between changes do vary unboundedly, otherwise no other method outperforms
random restarts of the Evolutionary Algorithm’s population after a change and the
whole objective of this thesis would be incompatible with the no-free-lunch theorem
for optimization (Wolpert & McReady, 1997). That is, while some approaches are
based on increasing population’s diversity after a change, this thesis focus on
maintaining diversity throughout the run — either by avoiding diversity loss, or by
introducing large amounts of genetic novelty in the population from time to time —,
removing the need to predict the changes and their severity. In addition, the thesis’s
proposals do not rely on memory schemes, and thus are expected to maintain a stable
behaviour in a wider spectrum of dynamics than memory-based GAs. (On the other
hand, by relying on ―blind‖ diversity maintenance strategies, it is expected that the
proposed algorithms experience some difficulties in dynamic optimization scenarios
where changes appear fast. This hypothesis is confirmed by the experiments.)
For that purpose, the research has looked for inspiration on natural phenomena, like
sexual reproduction strategies (see Chapter 3) and Self-Organized Criticality (Chapter
6), resulting in two distinct techniques that act upon mating — the Adaptive
4 In fact, the experiments were only conducted on Genetic Algorithms with binary codifications, but the extension to
other types of codification is trivial. 5 Black-box optimization algorithms need little or no information about a problem to solve it.
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Dissortative Mating Genetic Algorithm (ADMGA) — and mutation — Genetic
Algorithm with sand pile mutation (GASM).
ADMGA main feature is a selection and recombination scheme that avoids crossover
between similar individuals, leading to a slower decrease of the genetic diversity. This
way, diversity is maintained at a higher level. Unlike the Random Immigrants GA —
the main paradigm of diversity maintenance strategies — the proposed method works
by avoiding diversity loss, rather than introducing novelty in each iteration of the
algorithm.
The sand pile mutation replaces traditional mutation by an operator that is able to
introduce large amounts of genetic novelty in the population, in an undeterministic
manner. This behaviour is achieved by incorporating, in a traditional GA, a model that
is known to display a power-law proportion between the size of an event and its
frequency. Like random immigrants schemes, GASM deals with changing environments
by trying to supply the population with novel genetic material in a regular basis,
although not cyclic or predictable. However, and unlike Random Immigrants, this
proposal achieves that by spreading the new genes throughout the population, instead
of introducing new randomly generated elements in that same population.
To evaluate the efficiency of the proposed methods, the algorithms are tested in a
wide range of problems and dynamics (including trap deceptive functions, a class of
problems for which there are several experimental and theoretical studies, and which in
nowadays are the core of many studies on Evolutionary Computation), and compared to
other evolutionary techniques, including standard GAs and some classical methods for
dynamic optimization. In addition, the proposals are compared with two recently
proposed GAs for non-stationary function optimization: Elitism-based Immigrants GA
(EIGA) (Yang, 2008) and Self-Organized Random Immigrants GA (SORIGA) (Tinós
and Yang, 2007).
There is a huge amount of Evolutionary Algorithms specifically designed for
dynamic optimization, some with just minor changes when compared to standard
evolutionary approaches, and others that model rather complex strategies. It is not
possible to evaluate a new proposal against all these algorithms, and, in fact, it is not
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expected that any method outperforms all the others, especially in wide range of
problems. What is important here is to try to identify in which conditions a proposed
scheme may improve traditional GAs’ performance, and then confirm that assumption
with a proper experimental setup and check if the proposal is able to excel where other
algorithms are not.
SORIGA and EIGA appear in the test set for several reasons. First, they were selected
because they were published very recently — SORIGA in 2007 (Tinós & Yang, 2007)
and EIGA in 2008 (Yang, 2008) — in reputed international journals. SORIGA, in
particular, was chosen also because it is the approach closer to the sand pile mutation
and in (Tinós, 2007) it is stated that the algorithm is able to outperform other GAs on
several test problems. EIGA was selected because it is a very simple scheme, it does
not rely on complicated strategies, it only adds one parameter to the standard parameter
set (as it will be shown in the following chapter, the proposals of this thesis do not
increase the size of the parameter set) and the report (Yang, 2008) claims that EIGA is
able to outperform several other algorithms on dynamic problems. For all these
reasons, those two algorithms appear to be suited to accompany other strategies on the
extensive test set prepared for this work.
In the end, and after an intensive experimental study that explores all the
potentialities of traditional GAs, Random Immigrants GAs, Hypermutation schemes,
SORIGA and EIGA, it will be shown that the proposed algorithms are able to
outperform all other methods on a wide range of problems and dynamics, namely when
the changes are not very fast. In addition, SORIGA and EIGA will be shown to fail
when the test set examines a large amount of configurations, by setting the algorithms
parameter to different values. In particular, it will be shown how important mutation
rate and population size are for the behaviour of the algorithms. Population size, in
particular, is often neglected on many investigations on Evolutionary Algorithms and
dynamic optimization. This work stresses out the importance of having a population
size properly tuned, in order to avoid comparing suboptimal configurations of the
algorithms, and thus misleading the conclusions about the efficiency of the proposed
methods.
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The following section summarizes the contributions of this thesis, by briefly
describing the research process that lead to ADMGA and GGASM, and indicating the
peer-reviewed conference proceedings and international journals in which each step of
the investigations has been published.
1.2 Contributions
This thesis describes two strategies conceived to deal with non-stationary functions.
However, these investigations are only a part of a larger body-of-work, which studied
the behaviour of several algorithms on dynamic environments. Although they are not
described in the text, some parts of this research were crucial for the thesis, since they
inspired many ideas behind the proposed algorithms.
The first investigations and publications related with the complete body-of-work have
been focused on an Artificial Life model, proposed by Chialvo and Milonas (1995),
which simulates the stigmergic behaviour of a particular species of ants on a
homogenous habitat. Ramos and Almeida (2005) later extended Chialvo and Milonas’s
model in order to evolve it on digital image habitats and showed how the artificial
swarm can evolve, from local interactions, a complex global behaviour that allowed
them to ―recognize‖ the digital image, and even to react to changing images (that is, if
one replaces one image by another one, the swarm is able to readapt itself to the new
environment). However, due to an important characteristic of stigmergic systems —
memory — the model is not able to adapt to the second image as fast as it adapts to the
first habitat. In collaboration with Ramos, the author of this thesis introduced an
evolutionary mechanism that, together with the stigmergic nature of the model, greatly
increased its capability to adapt to changing environments. The results achieved by the
new model on changing digital images appear in (Fernandes et al., 2005b). Meanwhile,
the model was being adapted for mathematical function optimization. The results are
similar to those attained in image processing: the combination of stigmergy with
selective reproduction greatly improved the ability of the swarm to find the optimal
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regions of the fitness landscape. The first results on stationary and non-stationary
environments are in (Fernandes et al., 2005a). Later, Ramos, Fernandes and Rosa
(2005) tested the model on a wider range of dynamic environments6.
The results in (Fernandes et al., 2005a), (Fernandes et al., 2005b), (Ramos et al.,
2006) and (Ramos et al., 2007) inspired the idea of having a GA with varying
population size to tackle dynamic optimization problems7. Varying population GAs
have been studied since the beginning of 1990s, although most of the approaches have
reached a dead end. Fernandes and Rosa (2006) tried to overcome some of difficulties
of dynamic populations with the Self-Regulated Population Size Evolutionary
Algorithm (SRP-EA). The preliminary results on stationary environments were quite
promising, but in the meantime, a simpler and effective approach arose from SRP-EA,
simply by using a fixed size population. That algorithm is the aforementioned
ADMGA. The algorithm was first studied under a stationary optimization framework,
and the results were published in 2008 in the Journal of Soft Computing (Fernandes &
Rosa, 2008a). In the meantime, scalability tests with deceptive functions were
published as a book chapter in Advances in Evolutionary Computation (Fernandes &
Rosa, 2008b). In that same paper, the first studies on dynamic environments are
presented. Later, in (Fernandes et al., 2008e), the algorithm was applied to dynamic
trap and deceptive functions and a dynamic knapsack problem. Finally, an exhaustive
study on ADMGA and dynamic deceptive functions is in submission as a journal paper.
An alternative — and, as it will be shown, complementary — method for maintaining
the diversity of Genetic Algorithms throughout the run is proposed by Fernandes
6 Although the swarm is not suited (at least in its current form) for dynamic optimization — due to the high ratio
between the number of ants and the size of the search space —, its results, as aforementioned, inspired some of the
following investigations conducted for this thesis. In addition, other authors successfully applied the model to
image segmentation (Laptik & Navakauskas, 2007) and automated testing in software engineering (Mahanti &
Banerjee, 2006). Finally, the author of this thesis, in collaboration with Mora, Ramos, Merelo, Rosa and Laredo,
extended the model to deal with clustering and classification problems (Mora et al., 2008; Fernandes et al.,
2008f). The same model is also described in (Fernandes, 2008) and (Fernandes, 2009), where it is addressed as
potential creative tool, and where some possible dialogues between Art and Science are also mentioned. 7 The same results also inspired another line of work that mixed ideas from swarm algorithms with Estimation of
Distribution Algorithms (Lorrañga & Lozano, 2002; Pelikan et al., 1999). Those investigations led to some very
interesting results in (Fernandes et al., 2008b), (Lima et al., 2008a) and (Lima et al., 2008b), although they were
left out of this thesis. However, in a way, that research also inspired the sand pile mutation, because it deals with
self-organization and Evolutionary Algorithms.
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(Fernandes et al., 2008b) in collaboration with Merelo, Ramos and Rosa. The scheme,
called sand pile mutation, is based on the Self-Organized Criticality theory (Bak et al.,
1987; Bak, 1996) and maintains population diversity by engaging in a varying mutation
intensity that is driven by events holding a power-law proportion between their
magnitude and their abundance. The method — after some changes that enhanced its
performance when compared to the version presented in (Fernandes et al., 2008b) —
attains very good results on a wide range of dynamic problems, and, like ADMGA,
outperforms two recently proposed evolutionary approaches — in (Tinós & Yang,
2007) and (Yang, 2008a) — for dynamic optimization in most of the dynamic scenarios
in the test set. When hybridized, the sand pile mutation and ADMGA’s mating scheme
result in a highly efficient algorithm that outperforms both strategies on most of the
proposed dynamic scenarios. Appendix A shows the complete list of publications
related with the body of work developed during the making of this thesis.
Summarizing, this thesis contributes to the Evolutionary Computation research field
in general and the evolutionary dynamic optimization in particular with:
A new mating scheme, inspired by the behaviour of natural species, which
preserves diversity and improves GAs’ performance on many dynamic
scenarios. ADMGA is also shown to improve GAs’ scalability on stationary
deceptive trap functions. The algorithm’s main feature consists of a simple
self-regulated mechanism that does not add complexity to the tuning effort.
A new mutation operator, inspired by the Self-Organized Criticality theory,
which introduces diversity in the population, thus improving its ability to react
to changes. The distribution of the mutation rates depends on the type of
problem and also on the dynamics of changes. In addition, the distribution’s
shape is particularly suited for dynamic optimization, because in some
generations large amounts of genetic diversity are introduced in the
population. Raising the mutation rate is a classical strategy for evolutionary
dynamic optimization, but unlike previous approaches, GASM is self-
regulated.
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A hybrid scheme that mixes and improves the performance of both strategies.
By mixing the mating and selection scheme with the novel mutation operator,
the hybrid algorithm combines dissortative mating propensity to maintain
diversity with the sand pile mutation rate capability of introducing diversity.
The resulting hybrid broadens the range of dynamics in which each algorithm
excels.
Experimental studies that shed some light on how the GAs performance
varies with different mutation probability values and population size. To the
extent of our knowledge, there are no other studies on Evolutionary
Computation and dynamic optimization that investigate the effects of
parameter values in such a detail.
The algorithms do not increase GAs’ parameter space and do not require any
knowledge about the problem or dynamics of changes, as proposed.
The new methods improve the performance of not only traditional GAs, but
also the results of two state-of-the-art algorithms on many dynamic scenarios.
Finally, it is hoped that, due to the characteristics of the proposed methods —
the algorithms work without previous knowledge about the dynamics and do
not increase standard parameter space —, this line of work goes beyond
investigations on prototypes and inspire industrial applications.
1.3 Thesis Outline
This thesis is written in book-style with survey chapters and descriptions of
experimental studies. The survey chapters describe the Evolutionary Computation
research areas addressed by the thesis, and also the subjects which inspired some of the
techniques proposed in this work. Those survey chapters also try to put the present
research in perspective to other evolutionary approaches based on the natural systems.
The case study chapters are included to demonstrate the potential of the algorithms on
dynamic optimization problems.
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The remaining of the thesis is structured as follows: Chapter 2 gives a survey on
optimization in uncertain environments, with a special emphasis on dynamic
optimization, which is the main subject of the thesis. Several previously proposed
Evolutionary Algorithms and other bio-inspired methods for dynamic optimization are
described within a framework that divides them according to strategies to deal with
changes. Typical dynamic problems and dynamic problem generators are described, as
well as a number of criteria along which dynamic environments may be classified and
tested.
Chapter 3 focuses on dissortative mating strategies for Genetic Algorithms and
presents ADMGA, and Chapter 4 describes the experiments conducted with the
algorithm on a wide range of stationary environments. Scalability tests on deceptive
functions are also presented in Chapter 4, while Chapter 5 describes the experiments
and results on dynamic optimization problems.
Chapter 6 addresses Self-Organized Criticality models, describes some optimization
algorithms based on such theory and presents the sand pile mutation. Chapter 7
describes the results attained by this method on dynamic environments, and Chapter 8
proposes a hybrid algorithm that mixes ADMGA’s mating strategy and the new
mutation protocol.
Finally, Chapter 9 concludes the thesis and outlines plans for future research.
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Chapter 2
Optimization in Uncertain
Environments
2.1 Introduction
This chapter addresses evolutionary optimization in uncertain environments,
particularly those with time-varying (i.e., dynamic) fitness functions, which is the main
target of the investigations performed for the thesis. The most prominent types of
evolutionary techniques used in dynamic environments are described, along with their
specific fields of application, that is, the type of environmental dynamics for which the
different algorithms are more suited. Since robustness8 in dynamic optimization is the
main theme of the thesis, a special emphasis will be put on the limitations of some
efficient but, on the other hand, narrow-ranged methods. In addition, algorithms for
dynamic optimization of the same type of those described in the following chapter –
and used to evaluate the effectiveness of the proposed methods − are carefully
described. Classification of uncertain environments and bio-inspired algorithms for
dynamic optimization follows the ideas in (Jin & Branke, 2005) and (Branke, 2002).
The dynamic problem generator presented in (Yang, 2004) — used throughout this
investigation — and other well-known dynamic benchmark problems are also
described. The chapter ends with a critical note on experimental research methodology
for dynamic optimization. But first, a brief description of Evolutionary Computation in
8 In this context, robustness means that the efficiency of the algorithms is not limited to very specific kind of
dynamics. It may be stated that a robust algorithm is more suited for “black-box optimization”, that is, for dealing
with problems without using any previous knowledge about them. A different meaning for robustness is addressed
in the next section, under the different types of uncertain dynamics framework.
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general, and Genetic Algorithms (GAs) in particular, is provided. The description just
aims at introducing the basic concepts of GAs. The unfamiliarized reader may then
refer (Holland, 1975), (Goldberg, 1989a) and (Bäck, 1996) for more details on this
particular class of metaheuristics
2.2 Evolutionary Algorithms
As stated in the abstract of Bäck’s book Evolutionary Algorithms in Theory and
Practice (Bäck, 1996):
―Evolutionary Algorithms are a class of direct, probabilistic search and
optimization algorithms gleaned from the model of organic evolution. (...)‖
The main idea behind Evolutionary Algorithms is Charles Darwin’s (1809-1882)
theory of natural selection (Darwin, 1859). This class of algorithms is usually divided
into three sub-classes — GAs, Evolution Strategies and Genetic Programming — but
some features are common to them: selection of the best solutions in a population,
recombination and mutation9. This thesis deals mainly with GAs, and so the following
description will focus on that type of Evolutionary Algorithms. Although Evolution
Strategies and Genetic Programming comprise some features of their own, GAs are
sufficient to illustrate the general idea.
A GA is a population of candidate solutions to a problem that evolve towards optimal
(local or global) points of the search space by recombining parts of the solutions to
generate a new population. The decision variables of the problem are encoded in strings
with a certain length and cardinality. In GAs’ terminology, these strings are referred to
as chromosomes, each string position is a gene and its values are the alleles. The alleles
may be binary, integer, real-valued, etc, depending on the codification (which in turn
may depend on the type of problem).
9 Some Evolutionary Algorithms may work without recombination, others without mutation, but the general
framework can be defined in such a way.
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The ―best‖ parts of the chromosomes — or building-blocks — are guaranteed to
spread across the population by a selection mechanism that favours better (or fitter)
solutions. The quality of the solutions is evaluated by computing the fitness values of
the chromosomes, and this fitness function is usually the only information given to the
GA about the problem. This is the reason why GAs and other Evolutionary Algorithms
are so efficient as ―black-box-optimization‖ tools —