Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Uncertainty measures for evidential reasoning. II: A new measure of total uncertainty
International Journal of Approximate Reasoning
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Solving systems of nonlinear equations using the nonzero value of the topological degree
ACM Transactions on Mathematical Software (TOMS)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
OPTAC: a portable software package for analyzing and comparing optimization methods by visualization
Journal of Computational and Applied Mathematics
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Proceedings of the 3rd International Conference on Genetic Algorithms
Numerical Recipes: The Art of Scientific Computing with IBM PC or Macintosh
Numerical Recipes: The Art of Scientific Computing with IBM PC or Macintosh
Parallel Simulated Annealing Algorithms in Global Optimization
Journal of Global Optimization
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We propose a new metaheuristic, FRACTOP, for global optimization. FRACTOP is based on the geometric partitioning of the feasible region so that search metaheuristics such as Simulated Annealing (SA), or Genetic Algorithms (GA) which are activated in smaller subregions, have increased reliability in locating the global optimum. FRACTOP is able to incorporate any search heuristic devised for global optimization. The main contribution of FRACTOP is that it provides an intelligent guidance (through fuzzy measures) in locating the subregion containing the global optimum solution for the search heuristics imbedded in it. By executing the search in nonoverlapping subregions, FRACTOP eliminates the repetitive visits of the search heuristics to the same local area and furthermore, it becomes amenable for parallel processing. As FRACTOP conducts the search deeper into smaller subregions, many unpromising subregions are discarded from the feasible region. Thus, the initial feasible region gains a fractal structure with many space gaps which economizes on computation time. Computational experiments with FRACTOP indicate that the metaheuristic improves significantly the results obtained by random search (RS), SA and GA.