Impact of genetic algorithm's parameters on solution of numerical optimization benchmark problems
Mehtap Köse Ulukök · 2017
Genetic Algorithm (GA) is a well-known optimization and mostly used search method for both numerical and combinatorial optimization problems. Optimization is one of the main issue of most engineering problems. Genetic Algorithms (GA) is one of the mostly used method. There are many implementations of GA with different parameters resulting different performances. The aim of this study to compare the performance of GA with variety of population size and the mutation types. Mostly used five benchmark functions were used to test the performance of the GA. The experimental results show that with bigger population size and single point mutation operation cause better solutions on numerical optimization problems.