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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)
Genetic algorithms (GAs) play a major role in many artificial-life systems, but there is often little detailed understanding of why the GA performs as it does, and little theoretical basis on which to characterize the types of fitness landscapes that lead to successful GA performance. In this paper we propose a strategy for addressing these issues. Our(More)
We present results from experiments in which a genetic algorithm (GA) was used to evolve cellular automata (CAs) to perform a particular computational task—one-dimensional density classification. We look in detail at the evolutionary mechanisms producing the GA's behavior on this task and the impediments faced by the GA. In particular, we identify four "(More)
Copycat is a computer program designed to be able to discover insightful analogies, and to do so in a psychologically realistic way. Copycat's architecture is neither symbolic nor connectionist, nor a hybrid of the two; rather, the program has a novel type of architecture situated somewhere in between these extremes. It is an emergent architecture, in the(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)
We present results from an experiment similar to one perform ed by P ackard [24], in which a genetic algorit hm is used t o evolve cellular automata (CAs) to perform a particular computat iona l t ask. Packard exa mined the frequ ency of evolved CA rules as a function of Lan gton's A par am et er [17]; he interpret ed t he results of his experiment as(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 expressed as a Walsh(More)