Abstract
We present PANIC (Parallelism And Neural networks In Classifier systems), a parallel learning system which uses a genetic algorithm to evolve behavioral strategies codified by sets of rules. The fitness of an individual is evaluated through a learning mechanism, QCA (Q-Credit Assignment), to assign credit to rules. QCA evaluates a rule depending on the context where it is applied. This new mechanism, based on Q-learning and implemented through a multi-layer feed-forward neural network, has been devised to solve the rule sharing problem posed by traditional credit assignment methods. To overcome the heavy computational cost of this approach, we propose a decentralized and asynchronous parallel model of the genetic algorithm.
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Giani, A., Baiardi, F., Starita, A. (1995). Q-Learning and Parallelism in Evolutionary Rule Based Systems. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_98
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_98
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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