Journal
COMPUTERS IN INDUSTRY
Volume 41, Issue 2, Pages 113-127Publisher
ELSEVIER
DOI: 10.1016/S0166-3615(99)00046-9
Keywords
genetic algorithms; constraint handling; co-evolution; penalty functions; self-adaptation; evolutionary optimization; numerical optimization
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This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm (GA) for numerical optimization. The proposed approach produces solutions even better than those previously reported in the literature for other (GA-based and mathematical programming) techniques that have been particularly fine-tuned using a normally lengthy trial and error process to solve a certain problem or set of problems. The present technique is also easy to implement and suitable for parallelization, which is a necessary further step to improve its current performance. (C) 2000 Elsevier Science B.V. All rights reserved.
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