Genetic Algorithms Introduction-Computational Physics-Lecture Slides, Slides of Computational Physics

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Genetic Algorithms

Lecture - one

Dr. Nasir M Mirza

Computational Physics^ Computational Physics

Email:

[email protected]

Evolution •^

Here’s a simple description of how evolution works in biology

Organisms (animals or plants) produce a number of offspringwhich are almost, but not entirely, like themselves

Variation may be due to mutation (random changes)

Variation may be due to sexual reproduction (offspring have somecharacteristics from each parent)

Some of these offspring may survive to produce offspring of theirown—some won’t

The “better adapted” offspring are more likely to survive

Over time, later generations become better and better adapted

Genetic algorithms use this same process to “evolve” betterprograms

The behavior of an individual organism is an

inductive

inference about some yet unknown aspects of itsenvironment. If, over successive

generations, the

organism survives, we can say

that this organism is

capable of learning to predict

changes in its

environment.

„

The evolutionary approach is based on

computational

models of natural selection and

genetics. We call

them evolutionary

computation, an umbrella term that

combines

genetic algorithms, evolution strategies and

genetic programming.

Can evolution be intelligent?

Simulation of natural evolutionSimulation of natural evolution „

On 1 July 1858,

Charles Darwin

presented his

theory of evolution before the Linnean

Society of

London.

This day marks the beginning of a revolution

in biology.

„

Darwin’s classical

theory of evolution

, together with

Weismann’s

theory of natural selection

and

Mendel’s concept of

genetics

, now represent the

neo-Darwinian paradigm.

Evolution can be seen as a process leading to themaintenance of a population’s ability to surviveand reproduce in a specific environment. Thisability is called

evolutionary fitness

Evolutionary fitness can also be viewed as ameasure of the organism’s ability to anticipatechanges in its environment.

„

The fitness, or the quantitative measure of theability to predict environmental changes andrespond adequately, can be considered as thequality that is optimized in natural life.

Simulation of natural evolution^ Simulation of natural evolution

Let us consider a population of rabbits. Somerabbits are faster than others, and we may say thatthese rabbits possess superior fitness, because theyhave a greater chance of avoiding foxes, survivingand then breeding.

If two parents have superior fitness, there is a goodchance that a combination of their genes willproduce an offspring with even higher fitness.Over time the entire population of rabbits becomesfaster to meet their environmental challenges in theface of foxes.

How is a population with increasing fitness generated?^ How is a population with increasing fitness generated?

Genetic Algorithms •

Also known as

evolutionary algorithms

, genetic algorithms

demonstrate self organization and adaptation similar to the waythat the fittest biological organism survive and reproduce.

A genetic algorithm is an iterative procedure that represents itscandidate solutions as strings of genes called chromosomes.

Genetic Algorithms are often used to improve the performance ofother AI methods such as expert systems or neural networks.

The method learns by producing offspring that are better andbetter as measured by a fitness function, which is a measure of theobjective to be obtained (maximum or minimum).

Simulation of natural evolutionSimulation of natural evolution „

All methods of evolutionary computation simulatenatural evolution by

creating a population of individuals, „

evaluating their fitness, „

generating a new population through genetic operations, „

And repeating this process a number of times.

Classes of Search Techniques

Search Techniqes

Calculus Base

Techniqes

Guided random search

techniqes

Enumerative

Techniqes

BFS

DFS

Dynamic Programming

Tabu Search

Hill Climbing

SimulatedAnealing

EvolutionaryAlgorithms

Genetic

Programming

Genetic Algorithms

Fibonacci

Sort

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Stochastic operators

Selection

replicates the most successful solutions

found in a population at a rate proportional to theirrelative quality

Recombination

decomposes two distinct solutions

and then randomly mixes their parts to form novelsolutions

Mutation

randomly perturbs a candidate solution

Genotypes and phenotypes •

Genes are the basic “instructions” for building anorganism

A chromosome is a sequence of genes

Biologists distinguish between an organism’s genotype(the genes and chromosomes) and its phenotype (whatthe organism actually is like)

Example: You might have genes to be tall, but never grow to betall for other reasons (such as poor diet)

Similarly, “genes” may

describe

a possible solution to a

problem, without actually

being

the solution

Genotype space = {0,1}

L

Phenotype space

Encoding (representation)^ Decoding^ (inverse representation)

011101001 010001001

10010010 10010001

Representation

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Representation (cont) When choosing an encoding method rely on thefollowing key ideas •^

Use a data structure as close as possible to the naturalrepresentation

-^

Write appropriate genetic operators as needed

-^

If possible, ensure that all genotypes correspond to feasiblesolutions

-^

If possible, ensure that genetic operators preserve feasibility

Simple Genetic Algorithm^ •

Produce an initial population of individuals

Evaluate the fitness of all individuals

while

termination condition not met

do

Select fitter individuals for reproduction

Recombine between individuals

Mutate individuals

Evaluate the fitness of the modified individuals

Generate a new population

End while