Pattern recognition using evolution algorithms with fast simulated annealing
Pattern Recognition Letters
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
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VTS '98 Proceedings of the 16th IEEE VLSI Test Symposium
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ITC '99 Proceedings of the 1999 IEEE International Test Conference
LARES: An Artificial Chemical Process Approach for Optimization
Evolutionary Computation
Optimal capacitor placement for electric distribution systems
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
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Feasible approaches to the task of solving NP-complete problems usually entails the incorporation of heuristic procedures so as to increase the efficiency of the methods used. We propose a new technique, which incorporates the idea of simulated annealing into the practice of simulated evolution, in place of arbitrary heuristics. The proposed technique is called guided evolutionary simulated annealing (GESA). We report on the use of GESA approach primarily for combinatorial optimization. In addition, we report the case of function optimization, treating the task as a search problem. The traveling salesman problem is taken as a benchmark problem in the first case. Simulation results are reported. The results show that the GESA approach can discover a very good near optimum solution after examining an extremely small fraction of possible solutions. A very complicated function with many local minima is used in the second case. The results in both cases indicate that the GESA technique is a practicable method which yields consistent and good near optimal solutions, superior to simulated evolution