Why Use Elitism And Sharing In A Multi-objective Genetic Algorithm?

Elitism and sharing are two mechanisms that are believed to improve the performance of a multiobjective evolutionary algorithm (MOEA). Using a new empirical inquiry framework, this paper studies the effect of elitism and sharing design choices using a benchmark suite of two-criterion problems. Performance is assessed, via known metrics, in terms of both closeness to the true Pareto-optimal front and diversity across the front. Randomisation methods are employed to determine significant differences in performance. Informative visualisation of results is achieved using the attainment surface concept. Elitism is found to offer a consistent improvement in terms of both closeness and diversity, thus confirming results from other studies. Sharing can be beneficial, but can also prove surprisingly ineffective. Evidence presented herein suggests that parameter-less schemes are more robust than their parameter-based equivalents (including those with automatic tuning). A multi-objective genetic algorithm (MOGA) combining both elitism and parameter-less sharing is shown to offer high performance across the test suite.