Ppt on Genetic

8/3/2019 Ppt on Genetic

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INTRODUCTION TO GENETIC

ALGORITHMS

GROUP

RAKESH CHAORSIA-090101134(1-7)

SHUBHAM LOHAN-090101166(8-11)

RAVIKANT BIHARI-090101136(12-

16)


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GA CONCEPT

Genetic algorithm (GA) introduces theprinciple of evolution and genetics into searchamong possible solutions to given problem.

This is done by the creation within a machineof a population of individuals represented bychromosomes,in essence a set of character strings, that areanalogous to the DNA, that we have in our

own chromosomes.


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BASIC GENETIC ALGORITHM

Start with a large population of randomly generated

attempted solutions to a problem

Repeatedly do the following:

Evaluate each of the attempted solutions

Keep a subset of these solutions (the best ones) Use these solutions to generate a new population

Quit when you have a satisfactory solution (or you run out of

time)


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ALGORITHMIC PHASES

Evaluate Fitness

Yes

Initializepopulation

SatisfyConstrai

ns

Select SurvivorsModify Individuals

Evaluate Fitness

No


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CROSSOVER

Crossover is the similar to naturalreproduction.

Crossover combines genetic material from twoparents,in order to produce superior offspring.

Few types of crossover:

One-point

Multiple point.


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CROSSOVER

E.g.

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ParentParent 11 Parent 2Parent 2

00

11

5533

55

44

77

66

77

66

2244

22

33

00

11


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CROSSOVER

E.g.

7 / 76

00

11

2233

55

44

77

66

77

66

5544

22

33

00

11


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MUTATION

Mutation introduces randomness into thepopulation.

Why MutationThe idea of mutation is to reintroduce divergence

into a converging population.

Mutation is performed on small part ofpopulation, in order to avoid entering unstablestate.

In order to ensure that the individuals are notall exactly the same, you allow for a smallchance of mutation.


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MUTATION

11 11 00 11 00 1100 00

00 11 00 11 00 1100 11

11 00

00 11

ParentParent

ChildChild


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FITNESS FUNCTION

Fitness Function is the evaluation function thatis used to evaluated the solutions and find outthe better solutions.

Fitness of computed for each individual basedon the fitness function and then determinewhat solutions are better than others.

The fitness function is always problemdependent.


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SELECTION

The selection operation copies a singleindividual, probabilistically selected based onfitness, into the next generation of thepopulation.

Certain selection methods rate the fitness ofeach solution and preferentially select thebest solutions. Other methods rate only arandom sample of the population, as this

process may be very time-consuming. Several possible ways Keep the strongest

Keep some of the weaker solutions


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SELECTIONSURVIVAL OF THE STRONGEST

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0.930.93 0.510.51 0.720.72 0.310.31 0.120.12 0.640.64

Previous generationPrevious generation

Next generationNext generation

0.930.93 0.720.72 0.640.64


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STOPPING CRITERIA

Final problem is to decide when to stopexecution of algorithm.

Two possible ways.

First approach:Stop after production of definite number of

generations

Second approach:

Stop when the improvement in average fitnessover two generations is below a threshold


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ADVANTAGES OF GA

Advantages:-

It can solve every optimization problem whichcan be described with the chromosomeencoding.

It solves problems with multiple solutions.

Genetic algorithms are easily transferred toexisting simulations and models.

Genetic algorithm is a method which is veryeasy to understand and it practically does notdemand the knowledge of mathematics.


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DISADVANTAGES OF GA

Certain optimization problems (they are calledvariant problems) cannot be solved by meansof genetic algorithms. This occurs due topoorly known fitness functions which generate

bad chromosome blocks in spite of the factthat only good chromosome blocks cross-over.

There is no absolute assurance that a geneticalgorithm will find a global optimum. It

happens very often when the populationshave a lot of subjects.


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THANK YOU!!!

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Ppt on Genetic