A Genetic Algorithm with randomly shifted Gray codes and local optimizations based on quadratic approximations of the fitness

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Abstract

We present a Genetic Algorithm that we developed in order to address computationally expensive optimization problems. In order to accelerate this algorithm, we establish, generation after generation, quadratic approximations of the fitness in the close neighborhood of the best-so-far individual. We then inject in the population an individual that corresponds to the optimum of this approximation. We also introduce a modified mutation operator that acts on randomly-shifted Gray codes. We show that these techniques lead to the global optimum of typical benchmark problems in 5, 10 and 20 dimensions with a probability of success in one run of the order
of 95-97% and an average number of fitness evaluations of the order of 400−750×n, where n refers to the dimension of the problem.
LanguageEnglish
Pages195
Number of pages196
JournalProceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17)
DOIs
StatePublished - 2017

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Gray Code
Quadratic Approximation
Local Optimization
Fitness
Genetic Algorithm
Global Optimum
Accelerate
Mutation
Benchmark
Optimization Problem
Evaluation
Approximation
Operator

Keywords

    Cite this

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