On the choice of the parent population size.
Evolutionary algorithms (EAs) are population-based randomized search heuristics that often solve problems successfully. Here the focus is on the possible effects of changing the parent population size in a simple, but still realistic, mutation-based EA. It preserves diversity by avoiding duplicates in its population. On the one hand its behavior on well-known pseudo-Boolean example functions is investigated by means of a rigorous runtime analysis. A comparison with the expected runtime of the algorithm's variant that does not avoid duplicates demonstrates the strengths and weaknesses of maintaining diversity. On the other hand, newly developed functions are presented for which the optimizer considered that even a decrease of the population size by a single increment leads from efficient optimization to enormous runtime and overwhelming probability. This is proven for all feasible population sizes and thereby this result forms a hierarchy theorem. In order to obtain all these results new methods for the analysis of the EA are developed.
Storch
Tobias
T
Remote Sensing Technology Institute - MF, German Aerospace Center - DLR, Germany. tobias.storch@dlr.de
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005075:Biological Evolution; D019143:Evolution, Molecular; D014644:Genetic Variation; D006801:Humans; D016247:Information Storage and Retrieval; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D010363:Pattern Recognition, Automated; D011156:Population Density; D011336:Probability