### Abstract

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.

Language | English |
---|---|

Pages | 195 |

Number of pages | 196 |

Journal | Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17) |

DOIs | |

State | Published - 2017 |

### Fingerprint

### Keywords

### Cite this

}

**A Genetic Algorithm with randomly shifted Gray codes and local optimizations based on quadratic approximations of the fitness.** / Mayer, Alexandre.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A Genetic Algorithm with randomly shifted Gray codes and local optimizations based on quadratic approximations of the fitness

AU - Mayer,Alexandre

PY - 2017

Y1 - 2017

N2 - 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 orderof 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.

AB - 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 orderof 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.

KW - genetic algorithm

U2 - 10.1145/3067695.3075968

DO - 10.1145/3067695.3075968

M3 - Article

SP - 195

JO - Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17)

T2 - Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17)

JF - Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17)

ER -