Multiple-Population Genetic Algorithm for Solving Min-Max Optimization Problems
Mohammad Alshraideh, Luay H. Tahat · International Review on Computers and Software (IRECOS) · 2015
A min-max optimization problem was originally designed for simultaneous maximization of the same object functions during the same optimization run. The existing approaches for solving min-max problem using genetic algorithms is mainly focused on maintaining a single-population of candidate tests. In this paper, we explore a new approach for using genetic algorithms (GAs) to solve min-max problems. The approach uses a two-population GA to find Maximum and Minimum goals of separate search processes using distinct island populations. The advantage of the suggested approach is that its ability to explore a greater variety of execution paths increases the search efficiency under certain conditions. By applying this to a collection of benchmarks problems, it has been shown experimentally that the proposed multiple-population algorithm out performs the single-population algorithm in terms of the number of executions, execution time, performance improvement, and efficiency.