Evolutionary Optimization Using Graph Based Evolutionary Algorithms

Graph based evolutionary algorithms (GBEAs) are a novel evolutionary optimization technique that utilize population graphing to impose a topology or geography on the evolving solution set. In many cases in nature, the ability of a particular member of a population to mate and reproduce is limited. The factors creating these limits vary widely and include geographical distance, mating rituals, and others. The effect of these factors is to limit the mating pool, reducing the rate of spread of genetic characteristics, and increased diversity within the population. GBEAs mimic these factors resulting in increased diversity within the solution population. When properly tuned to the problem and the size of the population set, GBEAs can result in improved convergence times and a more diverse number of viable solutions. This paper examines the impact of the fitness landscape, population size, and choice of graph on the evolutionary process. In general, it was found that there was an optimal population size and graph combination for each problem. This optimal graph/population was problem dependent.Copyright © 2003 by ASME