Abstract
Pareto sort classification method is often used to compute the fitness value of evolutionary groups in multi-objective evolutionary algorithms. However this kind of computation may produce great selection pressure and result in premature convergence. To address this problem, an improved method to compute the fitness value of multi-objective evolutionary algorithms based on the relative relationship between objective function values is proposed in this paper, which improves the convergence and distribution of multi-objective evolutionary algorithms. Testing results of test functions show that the improved computation method has a higher ability of convergence and distribution than the evolutionary algorithm based on Pareto sort classification method.
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Wu, Y.G., Gu, W. (2009). Study on Improving the Fitness Value of Multi-objective Evolutionary Algorithms. In: Shi, Y., Wang, S., Peng, Y., Li, J., Zeng, Y. (eds) Cutting-Edge Research Topics on Multiple Criteria Decision Making. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02298-2_38
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DOI: https://doi.org/10.1007/978-3-642-02298-2_38
Publisher Name: Springer, Berlin, Heidelberg
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