Memetic Algorithms By  Anup Kulkarni( )  Prashanth Kamle( ) Instructor: Prof. Pushpak Bhattacharyya

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 2 Overview Philosophy Behind Memetics Genetic Algorithm – Intuition and Structure Genetic Algorithm Operators Memetic Algorithms TSP Using Memetic Algorithm

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 3 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.

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 4 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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 6 Biological Evolution Cultural Evolution..?? Source:

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 7 Biological Evolution Meme..!!!

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 8 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 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!!!

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 10 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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 11 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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 12 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.

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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 14 Crossover 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. offspring parents

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

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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 17 Solving the Traveling salesman problem with a Memetic Algorithm

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 18 Memetic Algo for TSP-representation Array pop stores population Size of pop=P No of cities=N Tour represented as N Fitness function-cost of the tour

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 19 TSP - Crossover Distance Preserving Crossover d(p1,p2) = d(p1,child) = d(p2,child) d(x, y) = #edges not common in x and y

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 20 Distance Preserving Crossover Source: B. Freisleben et al, “New Genetic Local Search Operators for the Traveling Salesman Problem”

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 21 2-OPT Search Delete any two edges Insert other two edges which will result in new tour

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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 23 Performance Source: Slides of A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Hybridisation with other techniques: Memetic Algorithms

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 24 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

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 25 References R. Dawkins, “The Selfish Gene – new edition”, Oxford University Press, 1989 pp David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition, Addison-Wesley Longman Publishing Co., 1989 pp 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 Springer, 1996 S. Lin and B. W. Kemighan, An effective heuristic algorithm for the Traveling Salesman problem, Operation Research 21 (1973)

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 26 ?

Anup Kulkarni and Prashanth K, Dept of CSE, IIT Bombay 27 Thank you!