A GA paradigm for learning fuzzy rules
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
Memetic algorithms: a short introduction
New ideas in optimization
A GA with heuristic-based decoder for IC floorplanning
Integration, the VLSI Journal
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A consensus-function artificial neural network for map-coloring
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A controlled genetic algorithm by fuzzy logic and belief functionsfor job-shop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Techniques for evolutionary rule discovery in data mining
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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We propose a Hybrid Genetic hill-climbing Algorithm (HGA) search algorithm and in this paper, demonstrated for n-region 4-coloring map problems. The HGA incorporates the usual Genetic Algorithm (with reproduction, crossover and mutation genetic operators) and a local hill-climbing algorithm. To effectively reduce the magnitude of the search space by 23 times (equivalent to better than one order of magnitude), in particular where n6, we propose a group representation that does not result in any loss of generality. We further propose an objective measure as a guide for the search process. To depict the efficacy of the proposed HGA algorithm, we compare its performance against the established standard Genetic Algorithm, Hill-climbing and an artificial neural network optimization algorithm for several n-region 4-color maps. We show that the proposed HGA is the only algorithm that is able to obtain an optimal solution for large maps (n500). Furthermore, we show that the proposed HGA is the fastest algorithm to yield an optimal solution in all n-region 4-color maps compared.