GAS, a concept on modeling species in genetic algorithms
Artificial Intelligence
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Shall We Repair? Genetic AlgorithmsCombinatorial Optimizationand Feasibility Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
UEGO, an Abstract Niching Technique for Global Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
GASUB: finding global optima to discrete location problems by a genetic-like algorithm
Journal of Global Optimization
Solving the multiple competitive facilities location and design problem on the plane
Evolutionary Computation
Heuristics for the facility location and design (1|1)-centroid problem on the plane
Computational Optimization and Applications
Parallel algorithms for continuous multifacility competitive location problems
Journal of Global Optimization
Information Sciences: an International Journal
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UEGO is a general clustering technique capable of accelerating and/or parallelizing existing search methods. UEGO is an abstraction of GAS, a genetic algorithm (GA) with subpopulation support, so the niching (i.e. clustering) technique of GAS can be applied along with any kind of optimizers, not only genetic algorithm. The aim of this paper is to analyze the behavior of the algorithm as a function of different parameter settings and types of functions and to examine its reliability with the help of Csendes' method. Comparisons to other methods are also presented.