Genetic_Algorithms

# Genetic_Algorithms - Genetic Algorithms Chapter 3 A.E Eiben...

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Genetic Algorithms Chapter 3

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A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms GA Quick Overview Developed: USA in the 1970’s Early names: J. Holland, K. DeJong, D. Goldberg Typically applied to: discrete optimization Attributed features: not too fast good heuristic for combinatorial problems Special Features: Traditionally emphasizes combining information from good parents (crossover) many variants, e.g., reproduction models, operators
A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms Genetic algorithms Holland’s original GA is now known as the simple genetic algorithm (SGA) Other GAs use different: Representations Mutations Crossovers Selection mechanisms

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A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms SGA technical summary tableau Representation Binary strings Recombination N-point or uniform Mutation Bitwise bit-flipping with fixed probability Parent selection Fitness-Proportionate Survivor selection All children replace parents Speciality Emphasis on crossover
A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms Genotype space = {0,1} L Phenotype space Encoding (representation) Decoding (inverse representation) 011101001 010001001 10010010 10010001 Representation

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A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms SGA reproduction cycle 1. Select parents for the mating pool (size of mating pool = population size) 1. Shuffle the mating pool 2. For each consecutive pair apply crossover with probability p c , otherwise copy parents 3. For each offspring apply mutation (bit-flip with probability p m independently for each bit) 4. Replace the whole population with the resulting offspring
A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms SGA operators: 1-point crossover Choose a random point on the two parents Split parents at this crossover point Create children by exchanging tails P c typically in range (0.6, 0.9)

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A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms SGA operators: mutation Alter each gene independently with a probability p m p m is called the mutation rate Typically between 1/pop_size and 1/ chromosome_length
A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms Main idea: better individuals get higher chance Chances proportional to fitness Implementation: roulette wheel technique Assign to each individual a part of the roulette wheel Spin the wheel n times to select n individuals SGA operators: Selection fitness(A) = 3 fitness(B) = 1 fitness(C) = 2 A C 1/6 = 17% 3/6 = 50% B 2/6 = 33%

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A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms An example after Goldberg ‘89 (1) Simple problem: max x 2 over {0,1,…,31} GA approach:
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## This note was uploaded on 05/07/2011 for the course INDUSTRIAL IE 208 taught by Professor Serolbulkan during the Fall '11 term at Marmara Üniversitesi.

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Genetic_Algorithms - Genetic Algorithms Chapter 3 A.E Eiben...

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