We develop an efficient differential evolution (DE) with neural networks-based approximating technique for computationally expensive problems, called DE-ANN hereinafter. We employ multilayer feedforward ANN to approximate the original problems for reducing the numbers of costly problems in DE. We also implement a fast training algorithm whose data samples use the population of DE. In the evolution process of DE, we combine the individual-based and generation-based methods for approximate model control. We compared the proposed algorithm with the conventional DE on three benchmark test functions. The experimental results showed that DE-ANN had capacity to be employed to deal with the computationally demanding real-world problems. © 2010 Springer-Verlag.
CITATION STYLE
Wang, Y. S., Shi, Y. J., Yue, B. X., & Teng, H. F. (2010). An efficient differential evolution algorithm with approximate fitness functions using neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6320 LNAI, pp. 334–341). https://doi.org/10.1007/978-3-642-16527-6_42
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