A synergistic approach for evolutionary optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
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As the evolutionary search progresses, it isimportant to avoid reaching a state where the geneticoperators can no longer produce superior offspring,prematurely. This is likely to occur when the searchspace reaches a homogeneous or near-homogeneousconfiguration converging to a local optimal solution.Maintaining a certain degree of population diversity iswidely believed to help curb this problem. Theproposed technique presented here, uses informedgenetic operations to reach promising, but un/under-exploredareas of the search space, while discouraginglocal convergence. Elitism is used at a different levelaiming at convergence. The proposed technique'simproved performance in terms solution precision andconvergence characteristics is observed on a numberof benchmark test functions with a genetic algorithm(GA) implementation.