Title

Neuroevolution: Evolving Neural Networks

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 recurrence, 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. Copyright is held by the author/owner(s).

Publication Date

1-1-2012

Publication Title

GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Number of Pages

805-826

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2330784.2330917

Socpus ID

84864981615 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84864981615

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