EVOLUTIONARY ALGORITHM PARAMETER FITNESS: AN EXPLORATORY STUDY

Genetic algorithms were the first evolutionary algorithm designed. These algorithms simulate natural selection to produce good solutions quickly for complex problems. Job shop scheduling with sequence dependent setup times is an NP-hard problem – any algorithm to optimize this problem has an exponential time. We explore which parameters for a genetic algorithm allow it to solve these job shop problems in limited time trials. Prior literature assumes the use of these parameters is beneficial, and the parameter values are selected either based on prior research values or from design of experiments on a limited range of parameter values. Multiple linear regression is used to determine which parameters can significantly improve solutions. Our results show that a proportional 50/50 crossover parameter and a large population size are the two parameters to obtain good solutions in a constrained time environment.