Abstract
In this paper, we introduce a new self-adaptive evolutionary algorithm for solving function optimization problems. The capabilities of the new algorithm include: a) self-adaptive choice of Gaussian or Cauchy mutation to balance the local and global search on the variable subspace, b) using multi-parent crossover to exchange global search information, c) using the best individual to take place the worst individual selection strategy to reduce the selection pressure and ensure to find a global optimization. These enhancements increase the capabilities of the algorithm to solve Shekel problems in a more robust and universal way. This paper will present some results of numerical experiments which show that the new algorithm is more robust and universal than its competitors.
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Lin, G., Kang, L., Chen, Y., McKay, B., Sarker, R. (2007). A Self-adaptive Mutations with Multi-parent Crossover Evolutionary Algorithm for Solving Function Optimization Problems. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_17
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DOI: https://doi.org/10.1007/978-3-540-74581-5_17
Publisher Name: Springer, Berlin, Heidelberg
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