Melanie Mitchell

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A simple evolutionary process can discover sophisticated methods for emergent information processing in decentralized spatially extended systems. The mechanisms underlying the resulting emergent computation are explicated by a technique for analyzing particle-based logic embedded in pattern-forming systems. Understanding how globally coordinated computation(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)
In this paper we review previous work and present new work concerning the relationship between dynamical systems theory and computation. In particular, we review work by Langton 21] and Packard 29] on the relationship between dynamical behavior and computational capability in cellular automata (CAs). We present results from an experiment similar to the one(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)
We describe an application of 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 classification and synchronization. In both cases, the GA discovered rules that gave rise to sophisticated emergent computational(More)
  • Jonathan Amsterdam, Peter Breedveld, Michael Caine, David Clemens, William Durfee, Derek Row-Ell +30 others
  • 1993
This report describes MM, a computer program that can model a variety of mechanical and uid systems. It addresses several issues: What is the appropriate input to the modeling process? How should the search for models be organized? What evidence can be brought to bear to constrain the task? MM takes as input both a description of the structure of the system(More)
Let us know how access to this document benefits you. Abstract Real-time processing of space-and-time-variant signals is imperative for perception and real-world problem-solving. In the brain, spatio-temporal stimuli are converted into spike trains by sensory neurons and projected to the neurons in subcortial and cortical layers for further processing.(More)
We extend the study of learning and generalization in feedforward Boolean networks [70, 93] to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical(More)