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Evolving neural networks

Published: 12 July 2011 Publication History

Abstract

Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: the state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, we will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to control, robotics, artificial life, and games.

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cover image ACM Conferences
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
July 2011
1548 pages
ISBN:9781450306904
DOI:10.1145/2001858

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Published: 12 July 2011

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Author Tags

  1. artificial life
  2. control
  3. evolutionary computation
  4. gaming
  5. neural networks
  6. neuroevolution
  7. robotics

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