Evolutionary computation
A data mining technique for data clustering based on genetic algorithm
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
A clustering technique for defect inspection
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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An evolutionary metaheuristic called genetic chromodynamics and its applications to optimization, clustering and classification are presented in current paper. Genetic chromodynamics aims at maintaining population diversity and detecting multiple optima. All algorithms derived from genetic chromodynamics use a variable-sized population of solutions and a local interaction principle as selection for reproduction. Subpopulation formation is achieved through the interaction between individuals, without any modification of the objective function. Sub-populations evolve and eventually converge to several optima. Very close individuals are merged and thus population size may be decreased with each generation. At convergence, each final subpopulation contains a single individual which corresponds to one optimum (solution of the problem). The model can be successfully applied to various optimization issues in telecommunication.