There are many approaches to solving multi-objective optimization problems using evolutionaryalgorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for sear...
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ISBN:
(纸本)0819441937
There are many approaches to solving multi-objective optimization problems using evolutionaryalgorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for searching in multidimensional objective spaces. First we suggest the use of linguistic variables to represent preferences and the use of fuzzy rule systems to implement tradeoff aggregations. After a review of alternatives EA methods for multi-objective optimizations, we explore the use of multi-sexual genetic algorithms (MSGA). In using a MSGA, we need to modify certain parts of the GAs, namely the selection and crossover operations. The selection operator groups solutions according to their gender tag to prepare them for crossover. The crossover is modified by appending a gender tag at the end of the chromosome. We use single and double point crossovers. We determine the gender of the offspring by the amount of genetic material provided by each parent. The parent that contributed the most to the creation of a specific offspring determines the gender that the offspring will inherit. This is still a work in progress, and in the conclusion we examine many future extensions and experiments.
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