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Attraction basin sphere estimating genetic algorithm for neuroevolution problems

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Abstract

In our previous work, we proposed a niching genetic algorithm: attraction basin sphere estimating genetic algorithm (ABSEGA). It can detect and recognize attraction basin spheres on a fitness landscape. Attraction basin spheres are used to improve the performance of niching. However, ABSEGA does not work well on Neuroevolution problems, because those problems are usually high dimensional and have numerous local optima. In this paper, we calculate the height of valleys to improve the niching results. The height is used to determine if an optimum is important, so we can track important optima even among hundreds of unimportant optima. We examine our method in a robotic arm problem, in which we evolve an artificial neural network to control the arm to catch balls. The results show that our method has a high ability to escape local optima and find relatively better solutions.

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Acknowledgments

The authors gratefully acknowledge financial support from China Scholarship Council.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

    Zhuoran Xu, Hiroyuki Iizuka & Masahito Yamamoto

Authors
  1. Zhuoran Xu
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  2. Hiroyuki Iizuka
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  3. Masahito Yamamoto
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Corresponding author

Correspondence to Zhuoran Xu.

Additional information

This work was presented in part at the 19th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 22–24, 2014.

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Xu, Z., Iizuka, H. & Yamamoto, M. Attraction basin sphere estimating genetic algorithm for neuroevolution problems. Artif Life Robotics 19, 317–327 (2014). https://doi.org/10.1007/s10015-014-0183-8

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  • DOI: https://doi.org/10.1007/s10015-014-0183-8

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