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Related Experiment Video

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

COVNET: a cooperative coevolutionary model for evolving artificial neural networks.

N Garcia-Pedrajas¹, C Hervas-Martinez, J Munoz-Perez

¹Dept. of Comput. and Numerical Anal., Cordoba Univ., Spain.

IEEE Transactions on Neural Networks

|February 2, 2008

Summary

COVNET, a cooperative coevolutionary model, evolves artificial neural networks by coevolving cooperating subnetworks. This approach yields better generalization and smaller networks compared to existing methods.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Evolving artificial neural networks (ANNs) often focuses on developing complete networks.
Existing models may have limitations in flexibility and adaptability for diverse architectures.
Cooperative coevolution offers a potential paradigm shift in ANN development.

Purpose of the Study:

Introduce COVNET, a novel cooperative coevolutionary model for evolving ANNs.
Investigate the efficacy of coevolving cooperating subnetworks for problem-solving.
Compare COVNET's performance against established methods like adaptive mixture of experts.

Main Methods:

COVNET employs cooperative coevolution of genetically isolated subnetworks.
Subnetworks cooperate to form complete network solutions.
The model allows flexible network structures and easy extensibility.

Main Results:

COVNET demonstrated superior generalization capabilities compared to the adaptive mixture of experts.
The model produced smaller networks than the adaptive mixture of experts.
COVNET achieved results comparable to or better than existing literature benchmarks.

Conclusions:

COVNET offers an effective approach to evolving ANNs through cooperative subnetwork evolution.
The model's flexibility supports diverse architectures and extensibility.
COVNET presents a promising alternative for developing efficient and high-performing ANNs.