Implicit fitness and heterogeneous preferences in the genetic algorithm

This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested autonomous agents. Reproductive decisions depend on subjective tradeoffs between the quality and quantity of offspring, avoiding the imposition of an external fitness function as typically used in genetic algorithms in favor of evolving, heterogeneous preferences over reproductive outcomes, expressed via reaction functions. When combined with a density-dependent economic or ecological problem, the implicit fitness approach draws a very different picture of "fitness" than other evolutionary algorithms. An application to learning in a repeated Cournot oligopoly game is developed analytically, predictions tested against a computational simulation. The result is an evolutionarily stable asymmetric equilibrium of much greater average profitability than concentrated Cournot-Nash collusion, while supporting...

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Justin T. H. Smith
• Keywords: GECCO 2010, Genetic Algorithms, Implicit Fitness, Implicit Fitness Approach, Optimization