## Evolutionary Computation: Principles and Practice for Signal ProcessingEvolutionary cmputation is one of the fastest growing areas of computer science, partly because of its broad applicability to engineering problems. The methods can be applied to problems as diverse as supply-chain optimization, routing and planning, task assignment, pharmaceutical design, interactive gaming, and many others within the signal processing domain. The text is an outgrowth of a series of SPIE short courses taught by the author. The examples span a range of applications and should be useful to a variety of readers of mixed backgrounds and expertise. |

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### Contents

Evolving Models of Time Series | 19 |

Evolutionary Clustering and Classification | 57 |

Evolving Control Systems | 95 |

Theory and Tools for Improving Evolutionary | 139 |

viii | 150 |

159 | |

### Common terms and phrases

annealing applied artificial neural networks average back propagation behavior binary representations classification clustering coefficients control strategy convergence criterion crossover D. B. Fogel dimensions equation estimated evaluated evolution evolutionary algorithm evolutionary computation Evolutionary Programming example Figure final best-evolved fitness distribution fixed-time metering freeway function fuzzy Gaussian random variable genetic algorithms hidden nodes Holland1 hyperbox IEEE incident indicates initial input iteration Jong et al.4 linear mainline MDL scores mean squared error Metal Sphere metering rates methods minimize neural networks noise nonlinear observed offered offspring fitness optimization outperform output parameters peak performance polynomial population possible prediction problem procedure proportional selection ramp control ramp demand ramp metering sample Scenario schemata self-adaptive sensor stations simulated simulated annealing simulated patient squared error standard deviation step theorem traffic traveling salesman problem trials typical values variance variation operators vector weights