Genetic Algorithm

PRESENTED BY- TAUSEEF AHAMDM.TECH (COMPUTER SC. & ENGINEERING)

COMPUTER ENGINEERING DEPARTMENTZAKIR HUSSAIN COLLEGE OF ENGG. & TECH.

A.M.U, ALIGARH

Genetic Algorithm

Outlines

A quick overview of GA Features of GA Various Methods of Population

Selection Anatomy Of GA An example of GA

References….

Adaptation in Neural and Artificial Systems, John Holland, 1975.

Genetic Algorithm in Search, Optimization and Machine Learning, David E. Goldberg, 1989.

C. Darwin. On the Origin of Species by Means of Natural Selection; or, the Preservation of flavored Races in the Struggle for Life. John Murray, London, 1859.

A quick overview of GA

Developed: USA in the 1970’s, by John Holland Holland’s original GA is now known as the

simple genetic algorithm (SGA) GA was inspired by process of biological

evolution It is based on the Darwin’s theory of “survival

of the fittest” : the better individuals have better chance of reproducing.

Features of GA

Used to solve Hard problems Maintains a POPULATION of solutions Solutions are encoded as

CHROMOSOMES REPRODUCTION creates a new

population members MUTATION and CROSSOVER occurs

during reproduction

Conceptual Algorithm

Population Selection

 stochastically select from one generation to create the basis of the next generation

The requirement is that the fittest individuals have a greater chance of survival than weaker ones

fitter individuals will tend to have a better probability of survival and will go forward to form the mating pool for the next generation

Various Methods of population Selection

a) Roulette Wheel selection b) Rank Selection c) Tournament Selection d) Elitism There are many other methods,

but we will discuss briefly only these methods.

Roulette Wheel selection(Example)

 Fitness f(x) of individual No. 3 is the fittest and No. 2 is the weakest

Strongest individual a value of 38% and the weakest 5%

These percentage fitness values can then be used to configure the roulette wheel

Number of times the roulette wheel is spun is equal to size of the population

Each time the wheel stops this gives the fitter individuals the greatest chance of being selected for the next generation and subsequent mating pool.

Individual No. 3: 01000001012 will become more prevalent in the general population because it is fitter

Tournament Selection Provides Selective pressure by holding

a tournament competition among n individuals

Best individual from tournament is one having highest fitness, which is the winner of tournament

Tournament competitions and winner is then inserted into mating pool

Tournament selection( Example)

Rank Selection  previous selection will have problems

when the fatnesses differs very much For example, if the best chromosome

fitness is 90% of all the roulette wheel then the other chromosomes will have very few chances to be selected

first ranks the population and then every chromosome receives fitness from this ranking

The worst will have fitness 1, second worst 2 etc. and the best will have fitness N(number of chromosomes in population).

Elitism Copies the best chromosome to new

offspring before the mutation and crossover

When creating a new population by crossover or mutation the best chromosome might be lost

Forces GA to retain some numbers of best individuals at each generation

Has been found that Elitism improves the performance significantly

An Example

Simple problem: max x2 over {0,1,…,31}

GA approach: Representation: binary code, e.g. 01101

13 Population size: 4 1-point xover, bitwise mutation Roulette wheel selection Random initialisation

We show one generational cycle done by hand

x2 example: selection

X2 example: crossover

Thank you……