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oai:riuma.uma.es:10630/111222026-02-03T11:06:14Zcom_10630_2254col_10630_37953

Chicano-García, José-Francisco Whitley, L. Darrell Tinós, Renato 2016-04-06T10:52:09Z 2016-04-06T10:52:09Z 2016-04-06 http://hdl.handle.net/10630/11122 0000-0003-1259-2990 Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand-alone search method, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time. This means that local search does not need to enumerate neighborhoods to find improving moves. It also means that evolutionary algorithms do not need to use random mutation as a operator, except perhaps as a way to escape local optima. In this paper, we show how improving moves can be identified in constant time for multiobjective problems that are expressed as k-bounded pseudo-Boolean functions. In particular, multiobjective forms of NK Landscapes and Mk Landscapes are considered. eng open access by-nc-nd Optimización matemática Programación (Matemáticas) Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization journal article