Exaptation as a Means of Evolving Complex Solutions

Biological exaptation is a theory which explains how certain complex solutions in nature are reached though a number of different fitness landscapes. This thesis explores how this theory may be useful in designing better algorithms to search for solutions to complex problems. Several exaptation-based genetic algorithms were developed for use in two different problem domains: dynamic landscapes and evolutionary artificial neural networks. Empirical evaluation of these algorithms suggests that exaptation can indeed be a powerful new paradigm in the field of evolutionary algorithms.

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