Hybrid steady state genetic algorithm for layout problem and the effect of using it on some types of crossover

Abstract EN

This paper investigates a genetic algorithm optimization for the layout problem to show the effect of some type of crossover approaches on the behavior of a steady state genetic algorithm for design optimization.

This algorithm is based on suitable techniques including solution encoding, evaluation function definition, and effective crossover operators.

It focuses on designing and representing issues of the problem and also shows the effects of some possible types of crossover operators.

A number of test cases using different implementations of genetic algorithms achieve interesting results which illustrate the performance of genetic algorithm present in this paper.

So, genetic algorithm (GA) is used in conjunction with local optimizer to find optimal spatial layouts.

Where selected the local optimizer is the best choice of refining GA solutions.

Different types of crossover used in the modified algorithm gain a better result than other types of crossover chosen.