This paper suggests a simple analogy between learning classifier systems (LCSs) and neural networks (NNs). By clarifying the relationship between LCSs and NNs, the paper indicates how techniques from one can be utilized in the other. The paper points out that the primary distinguishing characteristic of the LCS is its use of a co-adaptive genetic algorithm (GA), where the end product of evolution is a diverse population of individuals that cooperate to perform useful computation. This stands in contrast to typical GA/NN schemes, where a population of networks is employed to evolve a single, optimized network. To fully illustrate the LCS/NN analogy used in this paper, an LCS-like NN is implemented and tested. The test is constructed to run parallel to a similar GA/NN study that did not employ a co-adaptive GA. The test illustrates the LCS/NN analogy and suggests an interesting new method for applying GAs in NNs. Final comments discuss extensions of this work and suggest how LCS and NN studies can further benefit each other.

This content is only available as a PDF.
You do not currently have access to this content.