Helen G. Cobb

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Previous studies of Genetic Algorithm (GA) optimization in nonstationary environments focus on discontinuous, Markovian switching environments. This study introduces the problem of GA optimization in continuous, nonstationary environments where the state of the environment is a function of time. The objective of the GA in such an environment is to select a(More)
This paper begins to explore an analogy between the usual competitive learning metaphor presented in the GA literature and the cooperative learning metaphor discussed by Clearwater, et al. Examining the GA in the cooperative learning framework provides additional insight into the power of the algorithm. The illustrative empirical study reported in this(More)
Reinforcement Learning Methods (RLMs) typically select candidate solutions stochastically based on a credibility space of hypotheses which the RLM maintains, either implicitly or explicitly. RLMs typically have both inductive and deductive aspects: they inductively improve their credibility space on a stage-by stage basis; they deductively select an(More)
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