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Continual Coevolution Through Complexification (2002)
Kenneth O. Stanley
and
Risto Miikkulainen
In competitive coevolution, the goal is to establish an arms race'' that will lead to increasingly sophisticated strategies. However, in practice, the process often leads to idiosyncrasies rather than continual improvement. Applying the NEAT method for evolving neural networks to a competitive simulated robot duel domain, we will demonstrate that (1) as evolution progresses the networks become more complex, (2) complexification elaborates on existing strategies, and (3) if NEAT is allowed to complexify, it finds dramatically more sophisticated strategies than when it is limited to fixed-topology networks. The results suggest that in order to realize thefull potential of competitive coevolution, genomes must be allowed to complexify as well as optimize over the course of evolution.
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Citation:
In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002)
, William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and Riccardo Poli and Karthik Balakrishnan and Vasant Honavar and G{"u}nter Rudolph and Joachim Wegener and Larry Bull and Mitchell A. Potter and Alan C. Schultz (Eds.), pp. 8, San Francisco 2002. Morgan Kaufmann.
Bibtex:
@InProceedings{stanley:gecco02b, title={Continual Coevolution Through Complexification}, author={Kenneth O. Stanley and Risto Miikkulainen}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002)}, editor={William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and Riccardo Poli and Karthik Balakrishnan and Vasant Honavar and G{"u}nter Rudolph and Joachim Wegener and Larry Bull and Mitchell A. Potter and Alan C. Schultz}, address={San Francisco}, publisher={Morgan Kaufmann}, pages={8}, url="http://www.cs.utexas.edu/users/ai-lab?stanley:gecco02a", year={2002} }
People
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Kenneth Stanley
Postdoctoral Alumni
kstanley [at] cs ucf edu
Projects
NEAT: Evolving Increasingly Complex Neural Network Topologies
2000 - 2011
Areas of Interest
Evolutionary Computation
Neuroevolution
Reinforcement Learning
Software/Data
NEAT C++
The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i...
2010
NEAT C#
The SharpNEAT package contains C# source code for the NeuroEvolution of Augmenting Topologies method (see the original <...
2003
NEAT Matlab
The Matlab NEAT package contains Matlab source code for the NeuroEvolution of Augmenting Topologies method (see the orig...
2003
NEAT C++ for Microsoft Windows
The Windows NEAT package contains C++ source code for the NeuroEvolution of Augmenting Topologies method (see the origin...
2002
NEAT Java (JNEAT)
The JNEAT package contains Java source code for the NeuroEvolution of Augmenting Topologies method (see the original
2002
Labs
Neural Networks