Memetic Algorithms

Overview Philosophy Behind Memetics Genetic Algorithm – Intuition and Structure Genetic Algorithm Operators Memetic Algorithms TSP Using Memetic Algorithm

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.

Survival of The Fittest Origin of New Species Biological Evolution Natural Selection Survival of The Fittest Origin of New Species

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

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

Biological Evolution Meme..!!!

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

Examples of Meme Fashion Science Literature Music 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!!!

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

Genes and Memes, where they differ Genes are pre-decided Genes are static through generations, memes can be changed! Memes allow improvement After learning language, we contribute to it through literature New heuristics to 8-puzzle problem solved in class We use this property to improve genetic algorithms

Genetic Algorithm solves (typically optimization) problems by combining features of complete solutions to create new populations of solutions. applicable when it is hard or unreasonable to try to completely identify a subproblem hierarchical structure or to approach the problem via an exact approach.

Solving the Traveling salesman problem with a Memetic Algorithm

Memetic Algo for TSP-representation Array pop stores population Size of pop=P No of cities=N Tour represented as 1234....N Fitness function-cost of the tour

TSP - Crossover Distance Preserving Crossover d(p1,p2) = d(p1,child) = d(p2,child) d(x, y) = #edges not common in x and y

Distance Preserving Crossover Source: B. Freisleben et al, “New Genetic Local Search Operators for the Traveling Salesman Problem”

Hybridisation with other techniques: Memetic Algorithms Performance Source: Slides of A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Hybridisation with other techniques: Memetic Algorithms

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. Optimal solution=Genetic Algo + Local Search

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 B. Freisleben and P. Merz, New Genetic Local Search Operators for the Traveling Salesman Problem. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Proceedings of the 4th Conference on Parallel Problem Solving from Nature - PPSN IV, pages 890--900. Springer, 1996 S. Lin and B. W. Kemighan, An effective heuristic algorithm for the Traveling Salesman problem, Operation Research 21 (1973) 498- 516

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