Robotics 16

8/8/2019 Robotics 16

1/13

EXPERT SYSTEMS AND SOLUTIONS

Email: [email protected]

[email protected]

Cell: 9952749533www.researchprojects.info

PAIYANOOR, OMR, CHENNAI

Call For Research Projects Final

year students of B.E in EEE, ECE, EI,

M.E (Power Systems), M.E (Applied

Electronics), M.E (Power Electronics)

Ph.D Electrical and Electronics.

Students can assemble their hardware in our

Research labs. Experts will be guiding theprojects.


2/13

SymbolicArtificial

Intelligence

Lecture 10:

Geneticalgorithms

(continued)

Solution

to

previousexercise:


3/13

Selection methods

There are many ways

to select chromosomes

to survive to the next

generation

Roulette wheelselection: the better the

chromosome, the more

chance for selection it

possesses; imagine a

roulette wheel whereevery chromosome is

represented in

proportion to its fitness

function

Then a roulette ball is thrown

and selects chromosomes

chromosomes with bigger fitness

will be selected more times (if

duplicate chromosomes are

allowed).


4/13

Other selection methods

Rank selection: Roulette wheel selection does not work ifone chromosome has a fitness far in excess of the others;

Rank selection is better in these cases; rank selection first

ranks the population and then every chromosome receives

a fitness from this ranking (the method used in ourprevious examples);

Steady state selection: in every generation the best

chromosomes are selected for mutation and crossover, the

worst removed, and the remainder survive to the newgeneration;

Elitism: the best chromosomes always survive and the rest

are discarded - prevents losing good chromosomes.


5/13

Encoding methods

Chromosome A: 10110101010001010101

Chromosome B: 01010001010100101000

Encoding techniques (ways of representing the chromosome)

depend on the problem

binary encoding: the most commonly used - every

chromosome is a string of bits of 1s and 0s:

Binary encoding is efficient but not always natural;

sometimes corrections must be made after crossover andmutation to ensure that the genotype means something at the

phenotype level


6/13

Other encoding techniques

permutation encoding: every chromosome is a string of

numbers which represents a number in a sequence (the method

used in our examples in previous lecture):chromosome A: 1 5 3 2 6 5

chromosome B: 5 3 6 2 4 7

Again, corrections may be required after mutation and

crossover

value encoding: chromosomes can consist of different

types of value (e.g. real numbers, characters):

chromosome A: 1.232 3.45 2.65 0.454

chromosome B: ABDDDHSGHGSHGSGSHGSWE

chromosome C: (back) (right) (left) (forward)

Value encoding is useful for certain specialist problems (e.g.

evolving weights for neural networks), but requires special

mutation and crossover mechanisms


7/13

Crossover mechanisms


8/13

Mutation methods

bit inversion, e.g. 1000000001 =1010000000 where the third and

10th bits have been (randomly) mutated;

order changing, e.g. (5 6 3 4 7 3) = (5 3 4 6 7 3), where the

second, third and fourth values have been randomly scrambled;

value changing, e.g. (3.4 4.2 4.6 6.4 3.2) = (3.4 4.2 4.5 6.4 3.2)

where one value has been changed within a specific range


9/13

Further reading and references

Genetic algorithm toolbox for Matlab.

http://www.shef.ac.uk/~gaipp/ga-toolbox/

Tutorials with code:

http://evonet.dcs.napier.ac.uk/evoweb/resources/nutshell/

http://www.ai-junkie.com/gat1.htm

http://cs.felk.cvut.cz/~xobitko/ga/

http://www.secs.ex.ac.uk/resource/IT/ga.html (we have extensive

research interests in GAs and evolutionary computation)


10/13

Artificial Life (Alife)

Each cell has 8 possible neighbours.

1. If a cell is alive, it will survive

in the next generation if there

are either two or three

neighbours also alive.

2. If a cell is alive, it will die of

overcrowding if there are morethan three live neighbours.

3. If a cell is alive, it will die of

overexposure if there are fewer

than two of its neighbours

alive.

4. If a cell is dead, it will remain

dead unless exactly three of its

neighbours are alive, in which

case it will be born in the next

generation.

Life occurs on a virtual checkerboard. The

squares are cells. They are alive or dead.


11/13

Introduction to emergentism

An example of large scale events emerging from events

at a lower level.

The five live cells (shaded circles) are surrounded by

dead cells (open circles) on a virtual checkerboard, where

each cell has 8 neighbours. Following the rules

repeatedly, the initial configuration evolves over 4 steps to

produce a configuration where ...?


12/13

Exercise

Using the rules given previously, show three generations of

the following grid. Note that a blank cell means a dead

cell.


13/13

Solution

Robotics 16

Documents

Transcript of Robotics 16