Evolving neural networks through augmenting topologies.

Full Abstract

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

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Author information

Author/s: Stanley, Kenneth O (KO); Miikkulainen, Risto (R);

Affiliation: Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA. kstanley@cs.utexas.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Evolutionary computation (Evol Comput), published in United States. (Language: eng)

Reference: 2002-; vol 10 (issue 2) : pp 99-127

Dates: Created 2002/08/15; Completed 2002/12/13; Revised 2006/11/15;

PMID: 12180173, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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