1. Evolutionary algorithms
Prepared by
Jeethan & Jun

2. Contents Overview Evolutionary Algorithms (EA)
EA’s v/s Traditional search
Pseudo code
Parameters
Characteristics of EAs
Types of Eas
Advantages and disadvantages
References

3. Overview Search Problem Darwinian natural selection
Evolutionary Algorithms are population- based “generate-and-test” search algorithms

5. Evolutionary Algorithms (EAs)
Evolutionary algorithms operate on a population of potential solutions applying the principle of survival of the fittest to produce better approximations to a solution.
A type of Guided Random Search Used for optimization problems

6. EA’s v/s traditional search and optimization methods
search is performed in a parallel manner
Provides a number of potential solutions to a given problem.
They are generally more straight forward to apply
The final choice is left to the user

7. Pseudo code

8. Parameters influencing EA’s
Parameters of EAs may differ from one type to another. Main parameters:
Population size
Maximum number of generations
Elitism factor
Mutation rate
Cross-over rate

9. Characteristics of EAs
There are six main characteristics of EAs
Representation
Selection
Recombination
Mutation
Fitness Function
Survivor Decision
Representation:
How to define an individual
The way to store the optimization parameters.
Determined according to the problem.
Different types:
Binary representation
Real-valued representation
Lisp-S expression representation

10. Characteristics of EAs (cont)
Selection
Selection determines, which individuals are chosen for mating (recombination) and how many offspring each selected individual produces.
 Parents are selected according to their fitness by means of one of the following algorithms:
Roulette wheel selection
Truncation selection
Recombination
Determines how to combine the genes of selected parents
Types is determined according to the representation :
Bits of the genes
Values of the genes

11. Characteristics of EAs (cont)
Mutation
Change on a single gene of the individual
Fitness Function
Gives an intuition about how good the individual is.
Survivor Decision
Idea of survival of the best individuals. It is about Elitism factor.

12. Different Types of EAs Genetic Algorithms(GA) – binary strings
Genetic Programming(GP) – expression trees
Evolutionary Strategies(ES) – real- valued vectors
Evolutionary Programming(EP) – finite state machines

13. Evolutionary Algorithms Family
Genetic Programming
Evolutionary Programming
Genetic Algorithms
Evolutionary Strategies

14. Genetic Algorithms (GA)
Optimum parameter – Random strategy
Classified as global search heuristics
Represented by byte arrays
Two requirements
Genetic representation
Fitness function
Condition principal

15. Sample Application of GA
Finding the best path between two points in "Grid World"
Creatures in world:
Occupy a single cell
Can move to neighboring cells
Goal: Travel from the gray cell to the green cell in the shortest number of steps

16. Sample Application of GA
Population
Representation: N=00, E=10, S=11,W=01
Selection
Fitness function
Mutation
Cross over

17. Genetic Programming (GP)
find the proper program
simple problems – High computation power
represented by expression trees
mainly operate cross-over
mutation only can be applied once

18. Evolutionary Programming (EP)
no fixed representation
Only use mutation operation
child is determined in a way of mutation
So, we can conclude that there are three steps:
Initialize population and calculate fitness values
Mutate the parents and generate new population
Calculate fitness values of new generation and continue from the second step

19. EP mutation is very critical main application areas: not widely used
Cellular design problems.
Constraint optimization
Testing students’ code
......
not widely used

20. Evolutionary Strategies (ES)
Mainly use the real-vectors as coding representation
Very flexible
Representation: represent floating, real- vector as well
Selection: neighborhood method
plus selection (both parent and child)
comma selection (only parent)
Fitness function: objective function values.

21. ES
recombination & mutation: use additional parameters sigma represent the mutation amount
three recombination functions:
Arithmetic mean of the parents
Geometric mean of the parents
Discrete cross-over method.
There are many application areas of the ES. Some of them:Optimization of Road Networks
Local Minority Game
Multi-Criterion Optimization
.....

22. Disadvantages of Ea’s Advantages of Ea’s Large application domain
Complex search problems
Easy to work in parallel
Robustness
Disadvantages of Ea’s
Adjustment of parameters (trial-and-error)
No guarantee for finding optimal solutions in a finite amount of time

23. Videos about the application of EAs

24. References http://en.wikipedia.org/wiki/Evolutionary_algorithm
3.html

25. Questions ?