Genetic Algorithms

Presented By: Mudit Verma

Motivation

Initial States – Poor Solutions

Desired States – Better Solutions

Introduction

• A Search Heuristic & optimization solution

• Natural Evolution

Inheritance - Hereditary

Crossover – exchange of characteristics

Biological Mutation – Gene Alteration

Natural Selection - Survival of the Fittest

How it Works

• Five key phases

Initial Population

Fitness Function

Crossover

Mutation

Selection

Initial Population

• Population of Strings encoding characteristics

• Chromosome are represented in binary as strings of 0s and 1s. other encodings are also possible

• Initial Population may already be known or randomly generated

X1 X2 X3 X4 X5 … … … Xn

Chromosome or Genome

Individual Characteristics of a chromosome

Fitness Function

• A deterministic evaluation of a solution.

• Objective function to determine the merit of a solution.

• More fitness -> Better solution -> More probability to survive

0010 0101 1100 1000 1010

0110 0001 1101 0110 1111

0010 0111 1000 0011 1011

0000 1101 1101 0100 1110

10 14 4 1

Fitness Value

Crossover

X1 X2 X3 X4 X5 X6 X7 X8 X9

Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9

X1 X2 X3 X4 X5 X6 Y7 Y8 Y9

Y1 Y2 Y3 Y4 Y5 Y6 X7 X8 X9

Previous Generation

Next Generation

Crossover point

10

7

13

4

Selection

• In every generation fitness values chromosomes are sorted.

Most fit chromosomes survive to reproduce.

Rest are dropped from the population.

Survival of The Fittest

0010 0101 1100 1000 1010

0110 0001 1101 0110 1111

0010 0111 1000 0011 1011

0000 0111 1101 0110 1110

20 14 13 8

0010 1111 1000 0111 1010

0000 1101 1101 0101 1110

4 1

Mutation

• Mutation to maintain genetic diversity

• Mutation may happen

at one more or more places in chromosome

In many chromosomes in a generation

• Probabilistic

1 0 1 0 0 1 0

↓

1 0 1 0 1 1 0

Algorithm

1. Choose the initial population

2. Evaluate the fitness of each individual

3. Repeat until time limit, sufficient fitness achieved, saturation etc.

Select the best-fit individuals for reproduction

Breed new individuals through crossover and mutation operations to give birth to offspring

Evaluate the individual fitness of new individuals

Replace least-fit population with new individuals

Source: www.eis.uva.es/elena/newcomersGAs.htm

Evaluation

• Varies from Problem to Problem.

• Careful about

Encoding

Fitness Function

Mutation Probability

When to stop

Conclusion

• A tool for optimization & solution search problems.

• Applying real life evolution to engineering problems.

• Applications in

Bioinformatics

Computational Science

Gaming

Applied Physics

Economics & Finance

Chemistry

Manufacturing

Thank You !!