Cultural algorithms: theory and applications
New ideas in optimization
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-objective dynamic optimization with genetic algorithms for automatic parking
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A Cultural Algorithm with Operator Parameters Control for Solving Timetabling Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Minimal sets of quality metrics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Bi-objective combined facility location and network design
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Advances in Multi-Objective Nature Inspired Computing
Advances in Multi-Objective Nature Inspired Computing
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Cultural Algorithms (CAs) are one of the metaheuristics which can be adapted in order to work in multi-objectives optimization environments. On the other hand, Bi-Objective Uncapacitated Facility Location Problem (BOUFLP) and particularly Uncapacitated Facility Location Problem (UFLP) are well know problems in literature. However, only few articles have applied evolutionary multi-objective (EMO) algorithms to these problems and articles presenting CAs applied to the BOUFLP have not been found. In this article we presents a Bi-Objective Cultural Algorithm (BOCA) which was applied to the Bi-Objective Uncapacitated Facility Location Problem (BOUFLP) and it obtain an important improvement in comparison with other wellknow EMO algorithms such as PAES and NSGA-II. The considered criteria were cost minimization and coverage maximization. The different solutions obtained with the CA were compared using an hypervolume S metric.