Genetic Algorithms + Data Structures = Evolution Programs'What does your Master teach?' asked a visitor. 'Nothing,' said the disciple. 'Then why does he give discourses?' 'He only points the way - he teaches nothing.' Anthony de Mello, One Minute Wisdom During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively par allel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural net works. Recently (1-3 October 1990) the University of Dortmund, Germany, hosted the First Workshop on Parallel Problem Solving from Nature [164]. This book discusses a subclass of these algorithms - those which are based on the principle of evolution (survival of the fittest). In such algorithms a popu lation of individuals (potential solutions) undergoes a sequence of unary (muta tion type) and higher order (crossover type) transformations. These individuals strive for survival: a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations, the program converges - the best individual hopefully represents the optimum solution. There are many different algorithms in this category. To underline the sim ilarities between them we use the common term "evolution programs" . |
Contents
1 | |
Evolution Strategies and Other Methods | 8 |
What Are They? | 13 |
Evaluation function | 20 |
5 | 30 |
11 | 36 |
Conclusions | 81 |
Handling Constraints | 99 |
193 | 192 |
Machine Learning | 215 |
Conclusions | 231 |
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Common terms and phrases
Anthony de Mello applied approach arithmetical crossover average binary representation C₁ Chapter chro chromosome cities classical classifier constraints convergence crossover and mutation crossover operator crossover points data structures decoders defining length discuss domain edges element eval(v evaluation function evolution process evolution program evolution strategies example expected number experiments fitness floating point GAFOC GAMS gene genetic algorithm genetic operators GENETIC-2 GENOCOP graph GRAPH-2 heuristic hillclimbing implementation individuals initial integer iteration linear local optimum matched matrix methods modGA modified mutation operator nodes non-uniform mutation number of strings objective function offspring optimal control optimization problems optimum parameter parents performance pop_size population positions potential solutions prisoner's dilemma probability procedure random number randomly repair algorithm represents sampling schema schemata search space Section selected sequence simulated annealing single solution vector solve subtours technique tion tour transportation problem traveling salesman problem V₁ V₂ variable