1. Genetic Algorithms
Presented By: Mudit Verma

2. Motivation
Initial States – Poor Solutions
Desired States – Better Solutions

3. Introduction
• A Search Heuristic & optimization solution
• Natural Evolution
 Inheritance - Hereditary
 Crossover – exchange of characteristics
 Biological Mutation – Gene Alteration
 Natural Selection - Survival of the Fittest

4. How it Works
• Five key phases
Initial Population
Fitness Function
Crossover
Mutation
Selection

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Evaluation
• Varies from Problem to Problem.
• Careful about
Encoding
Fitness Function
Mutation Probability
When to stop

12. 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

13. Thank You !!