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Additional resources for Evolutionary Algorithms
Lozano, M. (1996). Adaptation of genetic algorithm parameters based on fuzzy logic controllers, in F. Herrera & J. Verdegay (eds), Genetic Algorithms and Soft Computing, Physica-Verlag HD, pp. 95–125. Holland, J. (1992). Adaptation in Natural and Artiﬁcial Systems, MIT Press, Cambridge. Hoos, H. & Stützle, T. (2005). Stochastic Local Search. Foundations and Applications, Elsevier, Oxford. Hynen, M. (1996). Exploring phenotype space through neutral evolution, Journal of Molecular Evolution 43: 165–169.
Here, the circular symbols represent the results obtained using NSGA-II or LEA in six figures. For case 1, BNH is a two-objective function problem with a convex Pareto front and constrained conditions are two inequalities. 5. 4 36 Evolutionary Algorithms shows that although a number of optimal solutions are obtained using NSGA-II, in terms of diversity of solutions, these solutions are not evenly spread out over the entire front. 4. The Pareto front consists of x1* = x2* ∈ [0,3], x1* ∈ [3,5] and x2* =3.
Here, the notable differences between curves of both HSA-EA are not observed. To determine what impact the neutral survivor selection has on results of the HSA-EA, a comparison between results of the HSA-EA with neutral survivor selection (Neutral) and the HSA-EA with deterministic survivor selection (Deter) was done. However, both versions of the HSA-EA run without local search heuristics. Results of these are represented in the Fig. 10. As reference point, the results of the original HSA-EA hybridized with the swap local search heuristic (Re f ) that obtains the overall best results are added to the ﬁgure.