Exploring human genome feature for improving genetic algorithm performance

Abstract

Genetic algorithm (i.e., GA) has longtermly obtained an extensive recognition for solving the optimization problem. Its pipelines process, which involves several operations, has been applied in many NP-hard problems, including the transportation network design problem (i.e., TNDP). As part of evolutionary computation methods, GA is inspired by Darwinian evolution, which is relied on the genetic operators (i.e., recombination, and mutation). On other side, the considerably achievement has been acquired by the genome researches, which offers an opportunity to deeply explore the recombination and mutation processes. This paper then presents variants of GA, which are inspired by the recent genome evidence of genetic operators. This exploration expectantly extends the benefit of evolution-based algorithm, which has been shown by the previous finding of GA. For examining the performance of proposed GA, the numerical experiment is involved for solving the TNDP. The performance comparisons show that the variation of crossover rate within a certain group of population provide better result than the standard GA.