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The building-block hypothesis states that the GA works well when short, low-order, highly-t schemas recombine to form even more highly t higher-order schemas. The ability to produce tter and tter partial solutions by combining building blocks is believed to be a primary source of the GA's search power, but the GA research community currently lacks precise(More)
If the interests of the funding agencies provide any sort of measuring stick for the coming of age of a discipline, then it would seem that the study of " complex systems " has recently become must-see science. You would be hard pressed to find in any collection of recent requests for proposals an organization that is not looking to explicitly leverage the(More)
What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increasingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship between GA performance and the structure of a given fitness function when it is is expressed as a Walsh(More)
Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutation-only genetic algorithm (GA) is introduced that identifies a new and general mechanism causing metastability(More)
We review recent work done by our group on applying genetic algorithms (GAs) to the design of cellular automata (CAs) that can perform computations requiring global coordination. A GA was used to evolve CAs for two computational tasks: density classiication and synchronization. In both cases, the GA discovered rules that gave rise to sophisticated emergent(More)
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 dynamics is derived in closed form. We show how finite populations induce periods of stasis-" fitness epochs "-and rapid jumps-" innovations ". The analysis(More)
How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task requiring globally-coordinated information processing. On most runs a class of relatively unsophisticated(More)