We introduce an analytical model that predicts the dynamics of a simple evolutionary algorithm in terms of the flow in the space of fitness distributions. In the limit of infinite populations the equations of motion are derived in closed form. We show how finite populations induce periods of...

This paper describes a computer program, called Copycat, that models how people make analogies. It might seem odd to include such a topic in a collection of papers mostly on the immune system. However, the immune system is one of many systems in nature in which a very large collection of...

In our work we are studying how genetic algorithms (GAs) can evolve cellular automata (CAs) to perform computations that require global coordination. The "evolving cellular automata" framework is an idealized means for studying how evolution (natural or computational) can create systems that...

"Nothing in biology makes sense except in the light of evolution." It would be hard to find a biologist today who disagrees with geneticist Theodosius Dobzhansky's famous claim. Darwin's theory of evolution via natural selection has done more than any other principle to explain the biological...

How does an evolutionary process interact with a decentralized, distributed system in order to produce globally coordinated behavior? Using a genetic algorithm (GA) to evolve cellular automata (CAs), we show that the evolution of spontaneous synchronization, one type of emergent coordination,...

We investigate the ability of a genetic algorithm to design cellular automata that perform computations. The computational strategies of the resulting cellular automata can be understood using a framework in which "particles" embedded in space-time configurations carry information and...

In this paper we review some previously published experimental results in which a simple hill-climbing algorithm---Random Mutation Hill-Climbing (RMHC)---significantly outperforms a genetic algorithm on a simple ``Royal Road'' function. We present an analysis of RMHC followed by an analysis of...

Genetic algorithms are computational models of evolution that play a central role in many artificial life models. We review the history and current scope of research on genetic algorithms in artificial life, using illustrative examples in which the genetic algorithms is used to study how...

We introduce a class of embedded-particle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances...