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Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations.

Motoaki Hiraga1, Masahiro Komura2, Akiharu Miyamoto2

  • 1Faculty of Mechanical Engineering, Kyoto Institute of Technology, Kyoto, Japan.

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Summary

This study enhances mutation-based neuroevolution for artificial neural networks by introducing self-adaptive mutation step sizes and adjusting structural mutation probabilities. These improvements boost performance and prevent topological bloat in evolving neural network architectures.

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Neuroevolution offers a gradient-free method for evolving artificial neural networks (ANNs), simultaneously optimizing topology and weights.
  • Traditional neuroevolution with topological evolution faces challenges with crossover due to the competing conventions problem.
  • Mutation-based neuroevolution avoids crossover, relying solely on mutations for genetic variation, presenting an alternative approach.

Purpose of the Study:

  • To enhance the performance of mutation-based artificial neural network evolution.
  • To introduce a self-adaptive mutation mechanism for improved exploration-exploitation balance.
  • To mitigate topological bloat by dynamically adjusting structural mutation probabilities based on network size.

Main Methods:

  • Implemented a self-adaptive mutation mechanism to automatically adjust mutation step size.
  • Developed a method to dynamically adjust structural mutation probabilities according to network size.
  • Evaluated the proposed methods on locomotion tasks using OpenAI Gym benchmarks.

Main Results:

  • The proposed self-adaptive mutation mechanism significantly improved performance compared to conventional neuroevolution algorithms.
  • Adjusting structural mutation probabilities effectively reduced topological bloat while sustaining performance.
  • The enhanced mutation-based approach demonstrated superior results in evolving neural network architectures.

Conclusions:

  • Self-adaptive mutation mechanisms and dynamic structural mutation probability adjustments are effective enhancements for mutation-based neuroevolution.
  • These methods offer a robust alternative to gradient-based approaches for ANN design.
  • The study highlights the potential of refined mutation strategies in advancing neuroevolutionary algorithms.