Post on 17-Jan-2016
Evolutionary Computation
Biologically inspired algorithms
BY:Andy GarrettYE Ziyu
What is Evolutionary Computation
• A subfield of artificial intelligence which mimics biology• Used in optimization of black box problems• Parallel processing
Types of Evolutionary Computation
• Evolutionary programing• Genetic algorithms• Evolutionary strategies• Genetic programing
• Genetic algorithms• Swarm intelligence
Genetic Algorithms
Genetic Algorithm——what is gene?
Biology:A certain DNA sequenceat a certain position of the chromosome.
Genetic Algorithm :A certain value of a certain element of the solution.
2 3 1 1 1
2 3 2 1 1
2 3 3 1 1
A certain element (an allele) of the solution (the chromosome)
Three alternative values (genes)
Genetic Algorithm——what is gene?
Biology Genetic Algorithm
Genes
Chromosome
Fitness of a individualIn the environment
Genes
Solution
Performance of a solutionin the problem. (Fitness)
constitute constitute
determines determines
Genetic Algorithm——what is gene?
In Genetic Algorithm, genes (values of elements of the solution) determine the fitness (performance) of a solution.
To solve a problem=
To find the combination of genes that provides the best fitness (performance)
Genetic Algorithm——Initiation
To conduct evolution,We need a set of solutions.(A population)
Initially, the population is generated randomly. This isthe first generation. A two-dimension search space
dotted by randomly generated solutions(each solution consists of two elements,
x and y)
X
Y
Genetic Algorithm——Reproduction: Crossover
Crossover is how we create new individuals from the existing ones.
2
3
1
4
4
1
2
2
1
3
Two solutions somehow be
selected as “parents”
2
3
1
4
4
1
2
2
1
3
Randomly selectone (or more)
point
2
3
2
1
3
1
2
1
4
4
Apply cross(Recombine the two solutions)
2
3
2
1
3
1
2
1
4
4
Finish!These will be two
Individuals inthe next generation
Genetic Algorithm——Reproduction: Selection
• Individuals with higher fitness have a higher probability to be chosen as parents of the crossover operation.
• Survival of the fittest
Genetic Algorithm——Reproduction: Selection
What’s the effect?
Genes associated with high fitness are more likely to be passed to the new generation.
After some generations, the average fitness of the population gets improved!
Genetic Algorithm——Reproduction: Selection
In a graphic view: (use our two-dimension example)
The population gathers aroundthe optimal solution.
It’s like that the population is climbing the hill.
Problem solved?
X
Y
Genetic Algorithm——Mutation
Problem: What if we have multiple hills in the searching
space?
The individuals may climb onto a hill that is not the highest.
Thus, they may gather around a local optimum.
Y
X
Y
(Local optimum)
(Global optimum)
Genetic Algorithm——Mutation
According to the crossover operation, genes in the new generation only come from the previous generation.
Thus, once the solutions gather around a local optimum, they will be constrained in its vicinity!
They won’t find the global optimum.
X
Y
(Constraining region)
Genetic Algorithm——Mutation
Mutation: Make random changes to some genes in each generation.
NEW genes are created!Solutions can jump out of the region.After some generations, they may probably gather around theglobal optimum.
X
Y
Genetic Algorithm——Scenario
Step 1: Initiation (Randomly generate the first generation);Step 2: Mutation;Step 3: Fitness evaluation; Step 4: Reproduction:
Selection;Crossover;
Step 5: Go back to step 2, repeat this loop until a sufficiently good solution is found.
Swarm intelligence
Swarm Intelligence
Swarm intelligence=
cognition of individuals + communication
Application in optimization problems:Particle Swarm Optimization (PSO)
Swarm Intelligence——Initiation
Randomly generate a set of solutions (called a swarm of particles),their initial positions,and their initial speeds.
X
Y
V2oV3o
V1o
Swarm Intelligence——Travelling
Two forces are exertedon each particle:
X
Y
1. Force pointing to the bestsolution this particle has everpassed through (pbest)2. Force pointing to the bestsolution any particle has everpassed through (gbest) pbest gbest
pbest1
pbest2(gbest)
pbest3
Swarm Intelligence——Travelling
Forces pointing to pbests:Fp1, Fp2, Fp3
These forces result from the cognition of individual particles.
X
Y
Fp3
Fp2
Fp1
Swarm Intelligence——Travelling
Forces pointing to gbests:Fg1, Fg2, Fg3
These forces result from the communication among the particles.
X
Y
Fg3 Fg2Fg1
Swarm Intelligence——Travelling
After some time, the particles would probablyfind some solutions thatare sufficiently close theglobal optimum.
X
Y
Fg3 Fg2Fg1Fp3
Fp2
Fp1
https://www.youtube.com/watch?v=j028fsZZZI4
Evolutionary Computation
• Time complexity is not generally considered• Number of iterations required for convergence
Questions?