Abstract
In recent years, many-objective optimization has become a popular research topic, after it was noted that algorithms that excelled in solving problems with two objectives were not suitable for problems with more than three objectives. In these more difficult problems, selection pressure towards the Pareto front deteriorates, leading to most solutions becoming non-dominated to each other, which makes selection very difficult. To overcome this, approximate measures, for example epsilon-dominance, relax the competition criteria between solutions and make it easier to eliminate worse solutions that would otherwise be non-dominated. In this paper, epsilon dominance is combined with genetic programming to solve a many-objective optimization problem for the first time. Results show that this combination is promising.
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Elkasaby, A., Salah, A., Elfeky, E. (2018). Approximate Dominance for Many-Objective Genetic Programming. In: Parlier, G., Liberatore, F., Demange, M. (eds) Operations Research and Enterprise Systems. ICORES 2017. Communications in Computer and Information Science, vol 884. Springer, Cham. https://doi.org/10.1007/978-3-319-94767-9_9
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