Chromosomemodification children modification modified

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Unformatted text preview: (R1 R2 R3 ... R22 R23) Program elements (genetic programming) ... any data structure ... Reproduction reproduction children parents population Parents are selected at random with selection chances biased in relation to chromosome evaluations. Chromosome Modification children modification modified children Modifications are stochastically triggered Operator types are: Mutation Crossover (recombination) Evaluation evaluated children modified children evaluation The evaluator decodes a chromosome and assigns it a fitness measure The evaluator is the only link between a classical GA and the problem it is solving Deletion population discarded members discard Generational GA: entire populations replaced with each iteration Steady­state GA: a few members replaced each generation A Simple Example “The Gene is by far the most sophisticated program around.” - Bill Gates, Business Week, June 27, 1994 A Simple Example The Traveling Salesman Problem: Find a tour of a given set of cities so that each city is visited only once the total distance traveled is minimized Representation Representation is an ordered list of city numbers known as an order­based GA. 1) London 3) Dunedin 5) Beijing 7) Tokyo 2) Venice 4) Singapore 6) Phoenix 8) Victoria CityList1 (3 5 7 2 1 6 4 8) CityList2 (2 5 7 6 8 1 3 4) Crossover Crossover provides means of “mixing” two parents (from selection step) to provide two offspring For given parents, crossover occurs with specified probability ≤...
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This note was uploaded on 04/05/2010 for the course CS 723 taught by Professor Sc during the Spring '10 term at Jaypee University IT.

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