Self-Adapting Evolutionary Parameters: Encoding Aspects for Combinatorial Optimization Problems

Evolutionary algorithms are powerful tools in search and optimization tasks with several applications in complex engineering problems. However, setting all associated parameters is not an easy task and the adaptation seems to be an interesting alternative. This paper aims to analyze the effect of self-adaptation of some evolutionary parameters of genetic algorithms (GAs). Here we intend to propose a flexible GA-based algorithm where only few parameters have to be defined by the user. Benchmark problems of combinatorial optimization were used to test the performance of the proposed approach.

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