Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm （EA）. However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. N...Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm （EA）. However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone （and vice-versa）.展开更多
文摘Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm （EA）. However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone （and vice-versa）.
