An efficient genetic algorithm for numerical function optimization with two new crossover operators

An efficient genetic algorithm for numerical function optimization with two new crossover operators

Автор: Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid

Журнал: International Journal of Mathematical Sciences and Computing @ijmsc

Статья в выпуске: 4 vol.4, 2018 года.

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Selection criteria, crossover and mutation are three main operators of genetic algorithm’s performance. A lot of work has been done on these operators, but the crossover operator has a vital role in the operation of genetic algorithms. In literature, multiple crossover operators already exist with varying impact on the final results. In this article, we propose two new crossover operators for the genetic algorithms. One of them is based on the natural concept of crossover i.e. the upcoming offspring takes one bit from a parent and next from other parent and continuously takes bits till last one. The other proposed scheme is the extension of two-point crossover with the concept of multiplication rule. These operators are applied for eight benchmark problems in parallel with some traditional crossover operators. Empirical studies show a remarkable performance of the proposed crossover operators.

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Genetic algorithms, Crossover operators, Benchmark functions, Comparison

Короткий адрес: https://sciup.org/15016679

IDR: 15016679   |   DOI: 10.5815/ijmsc.2018.04.04

Список литературы An efficient genetic algorithm for numerical function optimization with two new crossover operators

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