Abstract
In order to solve real-world optimization problems using real-coded genetic algorithm (RCGA), up to the level of satisfaction there have been attempts with hybrid crossover operators, replacement schemes, selection schemes and adaptive crossover operator probabilities. It is also possible to solve them by using efficient crossover (or recombination) operator. This operator can be a specialized to solve for particular type of problems. The neighborhood-based crossover operators used in RCGA are based on some probability distribution. In this paper, multi-parent recombination operators with polynomial and/or lognormal probability distribution are proposed. The performance of these operators is investigated on commonly used unimodal and multi-modal test functions. It is found that operators with multiple probability distributions are capable to solve problems very efficiently. The performance of these operators is compared with the performance of other operators. These operators are performing better than other operators.
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Raghuwanshi, M.M., Kakde, G.G. (2006). Multi-parent Recombination Operator with Multiple Probability Distribution for Real Coded Genetic Algorithm. In: Tiwari, A., Roy, R., Knowles, J., Avineri, E., Dahal, K. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36266-1_38
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DOI: https://doi.org/10.1007/978-3-540-36266-1_38
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