Abstract
In the field of estimation of distribution algorithms, choosing probabilistic model for optimizing continuous problems is still a challenging task. This paper proposes an improved estimation of distribution algorithm (HEDA) based on histogram probabilistic model. By utilizing both historical and current population information, a novel learning method – accumulation strategy – is introduced to update the histogram model. In the sampling phase, mutation strategy is used to increase the diversity of population. In solving some well-known hard continuous problems, experimental results support that HEDA behaves much better than the conventional histogram-based implementation both in convergence speed and scalability. Compared with UMDA-Gaussian, SGA and CMA-ES, the proposed algorithms exhibit excellent performance in the test functions.
The work is funded by National Key Project for Basic Research of China (G2002cb312205).
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Ding, N., Zhou, S., Sun, Z. (2006). Optimizing Continuous Problems Using Estimation of Distribution Algorithm Based on Histogram Model. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_69
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DOI: https://doi.org/10.1007/11903697_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
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