RT Journal Article
T1 Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization
A1 Chicano-García, José-Francisco
A1 Whitley, L. Darrell
A1 Tinós, Renato
K1 Optimización matemática
K1 Programación (Matemáticas)
AB 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.
YR 2016
FD 2016-04-06
LK http://hdl.handle.net/10630/11122
UL http://hdl.handle.net/10630/11122
LA eng
NO Chicano, F., Whitley D., & Tinós R. (2016).  Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization. 16th European Conference on Evolutionary Computation for Combinatorial Optimization (LNCS 9595), pp. 88-103
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.               Fulbright program, Ministerio de Educación (CAS12/00274), Ministerio de Economía y Competitividad (TIN2014-57341-R), Air Force Office of Scientific Research, Air Force Materiel Command, USAF (FA9550-11-1-0088), FAPESP (2015/06462-1) and CNPq.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026