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Memetic Algorithms

Elena Marchiori. Memetic Algorithms. Bio-Inspired Algorithms Course. Topics: Ant Colony Optimization (ACO) Memetic Algorithms Multi-objective Optimization Structure: Theory: learn above mentioned topics Practice: work in groups on application-oriented problems

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Memetic Algorithms

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  1. Elena Marchiori Memetic Algorithms

  2. Bio-Inspired Algorithms Course • Topics: • Ant Colony Optimization (ACO) • Memetic Algorithms • Multi-objective Optimization • Structure: • Theory: learn above mentioned topics • Practice: work in groups on application-oriented problems • See http://www.cs.ru.nl/~elenam/BIA.html

  3. Overview of today’s sample lesson on memetic algorithms Introduction Genetic Algorithm – Intuition and Structure Genetic Algorithm Operators Memetic Algorithms MAXclique Using Memetic Algorithm

  4. Genes and biological evolution A gene is a unit of biological information transferred from one generation to another. Genes determine our physical traits, what you look like, what you inherit from either one of your parents.

  5. Biological Evolution • Natural Selection • Survival of The Fittest • Origin of New Species

  6. Examples of Biological Evolution and Natural Adaptation • Frog Skin • Hollow Bones in Birds • Biological Evolution of Human • Characteristic Thumb • Erect Vertebral Column • Lower Jaw

  7. Biological Evolution Cultural Evolution..?? Source: www.wikipedia.org

  8. Biological Evolution Meme..!!!

  9. Meme • “the basic unit of cultural transmission, or imitation” - Richard Dawkins • “an element of culture that may be considered to be passed on by non-genetic means” - English Oxford Dictionary

  10. Examples of Meme • Fashion • Latest trends are ideas of fashion designers • Science • Scientists sharing their thoughts • Literature • Novel, poetry • Music • Even birds are found to imitate songs of other birds!!!

  11. Genes and Memes, where they are similar Genes propagate biologically from chromosome to chromosome Memes propagate from brain to brain via imitation Survival of fittest in meme Concept of God is survived though no scientific evidence is present

  12. Genes and Memes, where they differ Genes are pre-decided Genes are (almost always) static through generations, memes can be changed! Memes allow improvement After learning language, we contribute to it through literature New heuristics to search problems We use this property to improve algorithms

  13. Genetic Algorithm solves (typically optimization) problems by combining features of candidate solutions to create new populations of candidate solutions. applicable to complex optimization problems, e.g., when it is hard to tackle the problem via an exact search approach.

  14. Initialize population Pop Evaluate Pop Select Parents from Pop Recombine Parents Genetic Algorithm While not stop criterion do Evaluate Pop Return the best solution in Pop

  15. Crossover 0 0 0 1 1 1 1 1 1 1 0 0 0 0 • Purpose: to combine features of feasible solutions already visited in order to provide new potential candidate solutions with better objective function value. • Mechanism that restarts the search by “exploring” the space “between” solutions. parents offspring 0 0 0 0 0 0 0 1 1 1 1 1 1 1

  16. Mutation 1 1 1 1 1 1 1 before 1 1 1 0 1 1 1 after mutated gene value • Purpose: to introduce new characteristics in the population by random modifications. • Explores the “neighborhood” of a solution.

  17. Memetic Algorithm Initialize population Pop Optimize Pop(Local search) Evaluate Pop While not stop criterion do Select Parents from Pop Recombine Parents Optimize Pop(Local search) Evaluate Pop Return the best solution in Pop

  18. Solving the Maximum Clique problem with a Memetic Algorithm

  19. A Memetic algorithm for MAXclique

  20. GA main characteristics 1 2 • Representation: • 12345678 • 01011000 • Fitness: number of nodes of the sub-graph (=3 for the above sub-graph) • Selection: roulette-wheel (fitness proportional) • Elitism: copy best individual to next generation • Replacement rule: selects two best among parents and offsprings • Crossover: uniform • Mutation: swap values of two randomly selected genes 4 3 5 7 6 8

  21. The Local Search Module • Candidate solution is any subgraph in the population • Perturb the sub-graph • Extract a clique • Expand the clique

  22. Perturb added removed

  23. Extract a clique added perturb extract removed removed

  24. Enlarge to maximal clique perturb extract enlarge added added removed removed

  25. Conclusion A genetic algorithm promises convergence but not optimality. But we are assured of exponential convergence, possibly at different optimal chromosomes. Do very well in identifying the regions where those optima lie. Efficient and effective search: Genetic Algorithms + Local Search

  26. References R. Dawkins, “The Selfish Gene – new edition”, Oxford University Press, 1989 pp 189-201. David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition, Addison-Wesley Longman Publishing Co., 1989 pp 170-174. E. Marchiori . A Simple Heuristic Based Genetic Algorithm for the Maximum Clique Problem.ACM Symposium on Applied Computing ( SAC98), pp. 366-373, 1998.

  27. Questions?

  28. Thank you!

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