An Introduction to Cultural Algorithms

In this paper a computational model of the cultural evolution process is described. This model integrates several traditional approaches to modeling cultural evolution into a common conceptual framework. This framework depicts cultural evolution as a process of dual inheritance. At the micro-evolutionary level there is a population of individuals, each described in terms of a set of behavioral traits. Traits are passed from generation to generation at this level by means of a number of socially motivated operators. At the macro-evolutionary level, individuals are able to generate "mappa" that generalize on their experience. These individual mappa can be merged to form group mappa and these group mappa can be generalized and specialized using a variety of generic and problem specific operators. A specific implementation of Cultural Algorithms is described using Genetic Algorithms to represent the population space and Version spaces (or lattices) to represent the set of possible schemata that can be produced via generalizations on the population space. Individual and group mappa are defined as subspaces within the lattice. It is shown how the addition of a belief space to the traditional Genetic Algorithm framework can affect the rate at which learning can take place in terms of the modifications that it produces in the traditional schema theorem for Genetic Algorithms.