In general, Multi-objective Evolutionary Algorithms do not guarantee find solutions in the Pareto-optimal set. We propose a new approach for solving decomposable deceptive multi-objective problems that can find all solutions of the Pareto-optimal set. Basically, the proposed approach starts by decomposing the problem into subproblems and, then, combining the found solutions. The resultant approach is a Multi-objective Estimation of Distribution Algorithm for solving relatively complex multi-objective decomposable problems, using a probabilistic model based on a phylogenetic tree. The results show that, for the tested problem, the algorithm can efficiently find all the solutions of the Pareto-optimal set, with better scaling than the hierarchical Bayesian Optimization Algorithm and other algorithms of the state of art. © 2011 Springer-Verlag.
CITATION STYLE
Martins, J. P., Soares, A. H. M., Vargas, D. V., & Delbem, A. C. B. (2011). Multi-objective phylogenetic algorithm: Solving multi-objective decomposable deceptive problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6576 LNCS, pp. 285–297). https://doi.org/10.1007/978-3-642-19893-9_20
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