Improving Design Diversity Using Graph Based Evolutionary Algorithms

Graph based evolutionary algorithms (GBEAs) have been shown to have superior performance to evolutionary algorithms on a variety of evolutionary computation test problems as well as on some engineering applications. One of the motivations for creating GBEAs was to produce a diversity of solutions with little additional computational cost. This paper tests that feature of GBEAs on three problems: a real-valued multi-modal function of varying dimension, the plus-one-recall-store (PORS) problem, and an applied engineering design problem. For all of the graphs studied the number of different solutions increased as the connectivity of the graph underlying the algorithm decreased. This indicates that the choice of graph can be used to control the diversity of solutions produced. The availability of multiple solutions is an asset in a product realization system, making it possible for an engineer to explore design alternatives.