Evolutionary Computation Examples
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Evolutionary Computation
Evolutionary 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.
Applications of Evolutionary Computation
The two-volume set LNCS 14634 and 14635 constitutes the refereed proceedings of the 27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024, held as part of EvoStar 2024, in Aberystwyth, UK, April 3–5, 2024, and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EuroGP. The 51 full papers presented in these proceedings were carefully reviewed and selected from 77 submissions. The papers have been organized in the following topical sections: applications of evolutionary computation; analysis of evolutionary computation methods: theory, empirics, and real-world applications; computational intelligence for sustainability; evolutionary computation in edge, fog, and cloud computing; evolutionary computation in image analysis, signal processing and pattern recognition; evolutionary machine learning; machine learning and AI in digital healthcare and personalized medicine; problem landscape analysis for efficient optimization; softcomputing applied to games; and surrogate-assisted evolutionary optimisation.
Evolutionary Computation for Modeling and Optimization
Author: Daniel Ashlock
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-04-04
Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool.