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evolutionary algo's.ppt
- 1. Prepared by Jeethan & Jun 1
- 2. Overview Evolutionary Algorithms (EA) EA’s v/s Traditional search Pseudo code Parameters Characteristics of EAs Types of Eas Advantages and disadvantages References 2
- 3. Search Problem Darwinian natural selection Evolutionary Algorithms are population- based “generate-and-test” search algorithms 3
- 4. 4
- 5. 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 5
- 6. 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 6
- 7. 7
- 8. 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 8
- 9. 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 9
- 10. 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 10
- 11. 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. 11
- 12. Genetic Algorithms(GA) – binary strings Genetic Programming(GP) – expression trees Evolutionary Strategies(ES) – real- valued vectors Evolutionary Programming(EP) – finite state machines
- 14. Optimum parameter – Random strategy Classified as global search heuristics Represented by byte arrays Two requirements • Genetic representation • Fitness function Condition principal
- 15. 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 Finish Start
- 16. Representation: N=00, E=10, S=11,W=01 00 10 10 00 10 11 10 10 00 00 10 00 10 10 00 01 00 10 00 10 00 10 10 00 10 11 10 10 00 00 10 00 10 10 00 01 00 10 00 10 10 10 00 10 11 10 00 00 01 00 00 10 00 01 00 01 00 10 10 10 00 00 10 10 00 10 10 11 10 11 10 00 10 00 00 00 01 00 10 00 p1 = p2 = p2 = 10 10 00 10 11 10 00 00 01 00 00 10 00 01 00 01 00 10 10 10 00 00 10 10 00 10 10 11 10 11 10 00 10 00 00 00 01 00 10 00 p2 = p2 = 00 00 10 10 00 10 10 11 10 11 10 00 00 01 00 01 00 10 10 10 p1+2 = p1 = 00 11 01 10 10 00 00 01 00 10 00 10 11 10 00 00 10 00 10 10 p1’ = 00 11 00 10 10 00 10 01 00 10 00 10 11 10 00 00 11 00 10 10 Population Fitness function Mutation Selection Cross over
- 17. find the proper program simple problems – High computation power represented by expression trees mainly operate cross-over mutation only can be applied once
- 18. 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. mutation is very critical main application areas: ◦ Cellular design problems. ◦ Constraint optimization ◦ Testing students’ code ◦ ...... not widely used
- 20. 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. 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 ◦ ..... 21
- 22. 22 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
- 24. http://en.wikipedia.org/wiki/Evolutionary_algorithm http://www.geatbx.com/docu/algindex-02.html#TopOfPage http://www.faqs.org/faqs/ai-faq/genetic/part2/section- 3.html http://en.wikipedia.org/wiki/Genetic_programming http://alphard.ethz.ch/gerber/approx/default.html http://en.wikipedia.org/wiki/Evolutionary_programming http://en.wikipedia.org/wiki/Genetic_algorithm http://homepage.sunrise.ch/homepage/pglaus/gentore.htm http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html http://en.wikipedia.org/wiki/Evolution_strategy 24
- 25. ? 25