Abstract
In this paper we propose a novel multi-objective evolutionary algorithm that we call Potential Pareto Regions Evolutionary Algorithm (PPREA). Unlike state-of-the-art algorithms, which use a fitness assignment method based on Pareto ranking, the approach adopted in this work is new. The fitness of an individual is equal to the least improvement needed by that individual in order to reach non-dominance status.
This new algorithm is compared against the Nondominated Sorting Genetic Algorithm (NSGA-II) on a set of test suite problems derived from the works of researchers from MOEA community.
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Hallam, N., Kendall, G., Blanchfield, P. (2006). Solving Multi-objective Optimisation Problems Using the Potential Pareto Regions Evolutionary Algorithm. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_51
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DOI: https://doi.org/10.1007/11844297_51
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