Summary
This chapter considers a number of alternative methods for fitness assignment in evolutionary algorithms for multiobjective optimization. Most of the fitness assignment methods in the literature were designed to work for any number of objectives, in principle; but, in practice, some of the more popular methods (e.g. those in NSGA-II, IBEA and SPEA) do not perform well on problems with four or more objectives. We investigate why this is the case, considering two aspects of performance: convergence towards the Pareto front and drive towards a set of well spread solutions. The visualization of induced fitness surfaces is used to understand the effects of the different fitness assignment methods, and both Pareto- and non-Pareto-based methods are analysed.
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Hughes, E.J. (2008). Fitness Assignment Methods for Many-Objective Problems. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_15
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DOI: https://doi.org/10.1007/978-3-540-72964-8_15
Publisher Name: Springer, Berlin, Heidelberg
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