We combine local search algorithms with genetic algorithms. In this context local search can be thought of as learning over an individual's lifetime. We investigate two different ways of incorporating learning into the hybrid algorithm: Lamarckian evolution and the Baldwin effect. For each model we systematically vary the proportion of the population undergoing learning. We found that the quality of solution improves significantly at or above a critical level of learning.
|Title of host publication
|IECON Proceedings (Industrial Electronics Conference)
|IEEE, Institute of Electrical and Electronics Engineers
|Number of pages
|Published - Oct 2000