Prepared by Jeethan & Jun

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Overview Evolutionary Algorithms (EA) EA’s v/s Traditional search Pseudo code Parameters Characteristics of EAs Types of Eas Advantages and disadvantages References

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Search Problem

Darwinian natural selection

Evolutionary Algorithms are population-based “generate-and-test” search algorithms

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Evolutionary algorithms operate on a population of potential solutions applying the principle of survival of the fittest to produce better approximations to a solution.

A type of Guided Random Search Used for optimization problems

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search is performed in a parallel manner Provides a number of potential solutions to

a given problem. They are generally more straight forward to

apply The final choice is left to the user

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Parameters of EAs may differ from one type to another. Main parameters:

◦ Population size◦ Maximum number of generations◦ Elitism factor◦ Mutation rate◦ Cross-over rate

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There are six main characteristics of EAs◦ Representation◦ Selection◦ Recombination◦ Mutation◦ Fitness Function◦ Survivor Decision

Representation:

◦ How to define an individual◦ The way to store the optimization parameters.◦ Determined according to the problem.◦ Different types:

Binary representation Real-valued representation Lisp-S expression representation

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Selection

◦ Selection determines, which individuals are chosen for mating (recombination) and how many offspring each selected individual produces.

◦  Parents are selected according to their fitness by means of one of the following algorithms: Roulette wheel selection Truncation selection

Recombination

◦ Determines how to combine the genes of selected parents

◦ Types is determined according to the representation :

Bits of the genes Values of the genes

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Mutation◦ Change on a single gene of the individual

Fitness Function◦ Gives an intuition about how good the individual is.

Survivor Decision◦ Idea of survival of the best individuals. It is about

Elitism factor.

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Genetic Algorithms(GA) – binary strings

Genetic Programming(GP) – expression trees

Evolutionary Strategies(ES) – real-valued vectors

Evolutionary Programming(EP) – finite state machines

Evolutionary Algorithms

Genetic Algorithms

Genetic Algorithms

Genetic Programming

Genetic Programming

Evolutionary ProgrammingEvolutionary Programming

Evolutionary Strategies

Evolutionary Strategies

Optimum parameter – Random strategy Classified as global search heuristics Represented by byte arrays Two requirements• Genetic representation• Fitness function

Condition principal

Finding the best path between two points in "Grid World"

Creatures in world:◦ Occupy a single

cell◦ Can move to

neighboring cells

Goal: Travel from the gray cell to the green cell in the shortest number of steps

Finish

Start

Representation: N=00, E=10, S=11,W=01

00 10 10 00 10 11 10 10 00 00 10 00 10 10 00 01 00 10 00 10 00 10 10 00 10 11 10 10 00 00 10 00 10 10 00 01 00 10 00 10

10 10 00 10 11 10 00 00 01 00 00 10 00 01 00 01 00 10 10 10

00 00 10 10 00 10 10 11 10 11 10 00 10 00 00 00 01 00 10 00

p1 =

p2 =

p2 =

10 10 00 10 11 10 00 00 01 00 00 10 00 01 00 01 00 10 10 10

00 00 10 10 00 10 10 11 10 11 10 00 10 00 00 00 01 00 10 00 p2 =

p2 =

00 00 10 10 00 10 10 11 10 11 10 00 00 01 00 01 00 10 10 10 p1+2 =

p1 = 00 11 01 10 10 00 00 01 00 10 00 10 11 10 00 00 10 00 10 10

p1’ = 00 11 00 10 10 00 10 01 00 10 00 10 11 10 00 00 11 00 10 10

Population

Fitness function Mutation

Selection

Cross over

find the proper program simple problems – High computation power represented by expression trees mainly operate cross-over mutation only can be applied once

no fixed representation Only use mutation operation child is determined in a way of mutation So, we can conclude that there are three

steps:◦ Initialize population and calculate fitness values ◦ Mutate the parents and generate new population◦ Calculate fitness values of new generation and

continue from the second step

mutation is very critical main application areas:

◦ Cellular design problems.◦ Constraint optimization◦ Testing students’ code◦ ......

not widely used

Mainly use the real-vectors as coding representation

Very flexible Representation: represent floating, real-

vector as well Selection: neighborhood method

◦ plus selection (both parent and child)◦ comma selection (only parent)

Fitness function: objective function values.

recombination & mutation: use additional parameters sigma represent the mutation amount

three recombination functions:◦ Arithmetic mean of the parents◦ Geometric mean of the parents◦ Discrete cross-over method.

There are many application areas of the ES. Some of them:Optimization of Road Networks◦ Local Minority Game◦ Multi-Criterion Optimization◦ .....

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Advantages of Ea’s

• Large application domain • Complex search problems• Easy to work in parallel• Robustness

Disadvantages of Ea’s• Adjustment of parameters (trial-and-error) No guarantee for finding optimal solutions in a finite amount of time

https://www.youtube.com/watch?v=ejxfTy4lI6I

http://en.wikipedia.org/wiki/Evolutionary_algorithm http://www.geatbx.com/docu/algindex-02.html#TopOfPage http://www.faqs.org/faqs/ai-faq/genetic/part2/section-3.html http://en.wikipedia.org/wiki/Genetic_programming http://alphard.ethz.ch/gerber/approx/default.html http://en.wikipedia.org/wiki/Evolutionary_programming http://en.wikipedia.org/wiki/Genetic_algorithm http://homepage.sunrise.ch/homepage/pglaus/gentore.htm http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html http://en.wikipedia.org/wiki/Evolution_strategy

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