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A self-adaptive evolutionary algorithm (SAEA) for solving nonlinear programming problems. (English) Zbl 1175.90374

Ladde, G. S. (ed.) et al., Proceedings of neural, parallel, and scientific computations. Vol. 3. Papers based on the presentations at the 3rd international conference, Atlanta, GA, USA, August 09–12, 2006. Atlanta, GA: Dynamic Publishers (ISBN 1-890888-02-8/pbk). 151-156 (2006).
Summary: We introduce a new self-adaptive evolutionary algorithm (SAEA) for solving nonlinear programming (NLP) problems. The capabilities of the new algorithm include: a) self-adaptive to choice 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 nonlinear programming problems in a more robust and universal way. This paper present some results of numerical experiments which show that the new algorithm is more robust and universal than its competitors.
For the entire collection see [Zbl 1130.68011].

MSC:

90C30 Nonlinear programming
90C59 Approximation methods and heuristics in mathematical programming
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