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Genetic Algorithms and Neural Networks : A Comparison Based on the Repeated Prisoner ’ s Dilemma

Genetic Algorithms (GAs) and Neural Networks (NNs) in a wide sense both belong to the class of evolutionary computing algorithms that try to mimic natural evolution or information handling with respect to everyday problems such as forecasting the stock market, firms’ turnovers, or the identification of credit bonus classes for banks. Both methods have gained more ground in recent years, especially with respect to micro-economic questions. But owing to the dynamics inherent in their evolution, they belong to somewhat disjunct development communities that interact seldom. Hence comparisons between the different methods are rare. Despite their obvious design differences, they also have several features in common that are sufficiently interesting for the innovation-oriented to follow up and so to understand these commonalities and differences. This paper is an introductory demonstration of how these two methodologies tackle the wellknown Repeated Prisoner’s Dilemma.