Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Evolving neural networks through augmenting topologies
Evolutionary Computation
Incremental evolution of trainable neural networks that are backwards compatible
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
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An adaptive algorithm for drifting environments is proposed and tested in simulated environments. Two powerful problem solving technologies namely Neural Networks and Genetic Algorithms are combined to produce intelligent agents that can adapt to changing environments. Online learning enables the intelligent agents to capture the dynamics of changing environments efficiently. The algorithm's efficiency is demonstrated using a mine sweeper application. The results demonstrate that online learning within the evolutionary process is the most significant factor for adaptation and is far superior to evolutionary algorithms alone. The evolution and learning work in a cooperating fashion to produce best results in short time. It is also demonstrated that online learning is self sufficient and can achieve results without any pre-training stage. When mine sweepers are able to learn online, their performance in the drifting environment is significantly improved. Offline learning is observed to increase the average fitness of the whole population.