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Evolving Neural Networks through Augmenting Topologies
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 signicantly 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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Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
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Metadata
- Authors
- Stanley, K. O., & Miikkulainen, R.
- Venue
- Evolutionary Computation
- Publisher
- MIT Press - Journals
- Published
- 2002-06
- Volume
- 10
- Issue
- 2
- Pages
- 99-127
- ISSN
- 1063-6560 (ISSN-L)
- 1530-9304
- OpenAlex
- W2111935653
- Cited by
- 3,258
- References
- 2
- FWCI
- 24.42
- Type
- journal article
- Language
- en
- Open access
- closed
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