Interactive Genetic Algorithms for Optimization of Problems with Multiple Modes and Implicit Performance Indices
作者： Dunwei Gong , Jie Yuan
关键词: Mathematical optimization 、 Theoretical computer science 、 Meta-optimization 、 Genetic algorithm 、 Property (programming) 、 Quality control and genetic algorithms 、 Fitness function 、 Effective method 、 Computer science 、 Population 、 Genetic representation
摘要: Optimization of problems with multiple modes and implicit performance indices is common, but so far there's still no effective method to solve it. An interactive genetic algorithm (IGA) for optimizing such presented in this paper. Firstly, according the distribution fitness individuals an evolutionary population, property number optimized problem are determined, optimal all conserved. Then a user assigns offspring obtained by evolving one generation referring phenotypes these reserved individuals. The applied fashion design experimental results validate its efficiency. achievement paper provides feasible approach looking several solutions indices.
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