Evolving Cellular Automata: With Genetic Algorithms

本研究的目标是确定遗传算法在 设计空间扩展的并行系统来执行计算任务。一个重要 该项目的一个新组成部分是发展适当的理论框架 为了理解遗传进化系统的计算策略， 算法以及设计这种策略的进化过程。 这 研究将作出三个主要贡献：（1）在空间上如何研究的进展 扩展的并行系统执行计算;（2）理论和实践的进展， 遗传算法;（3）应用于并行计算问题的基础， 如图像处理、降噪、纹理检测、相干性自动发现等 图像时间序列中的结构。

The goals of this research are to determine the usefulness of genetic algorithms in designing spatially extended, parallel systems to perform computational tasks. An important and novel component of this project is the development of appropriate theoretical frameworks for understanding both the computational strategies of the systems evolved by the genetic algorithm as well as the evolutionary process by which such strategies are designed. This research will make three major contributions: (1) advances in the study of how spatially extended parallel systems perform computations; (2) advances in the theory and practice of genetic algorithms; and (3) a foundation for applications to parallel computation problems such as image processing, noise reduction, texture detection, and the automatic discovery of coherent structures in image time series.