Reducing the search time of a steady state genetic algorithm using the immigration operator

Abstract

An examination is made of the fundamental trade-off between exploration and exploitation in a genetic algorithm (GA). An immigration operator is introduced that infuses random members into successive GA populations. It is theorized that immigration maintains much of the exploitation of the GA while increasing exploration. To test this theory, a set of functions that often require the GA to perform an excessive number of evaluations to find the global optimum of the function is designed. For These functions, it is shown experimentally that a GA enhanced with immigration (1) reduces the number of trials that require an excessive number of evaluations and (2) decreases the average number of evaluations needed to find the optimum function.<>