Learning With Case-Injected Genetic Algorithms

Authors:
  • JMJ. McDonnell
    J. McDonnell
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Abstract
This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a GA's population with appropriate intermediate solutions to similar previously solved problems. Perhaps, counterintuitively, simply injecting solutions to previously solved problems does not produce very good results. We provide a framework for evaluating this GA-based machine-learning system and show experimental results on a set of design and optimization problems. These results demonstrate the performance gains from our approach and indicate that our system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization.

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