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Book ChapterDOI

An Introduction to Evolutionary Computation

David B. Fogel
- pp 1-28
TLDR
This chapter contains sections titled: References An Introduction to Simulated Evolutionary Optimization Evolutionary Computation: Comments on the History and Current State.
Abstract
This chapter contains sections titled: References An Introduction to Simulated Evolutionary Optimization Evolutionary Computation: Comments on the History and Current State

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Citations
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Book ChapterDOI

Parameter Control in Evolutionary Algorithms

TL;DR: A classification of different approaches based on a number of complementary features is provided, and special attention is paid to setting parameters on-the-fly, which has the potential of adjusting the algorithm to the problem while solving the problem.
Journal ArticleDOI

Recent advances in differential evolution: a survey and experimental analysis

TL;DR: Numerical results show that, among the algorithms considered in this study, the most efficient additional components in a DE framework appear to be the population size reduction and the scale factor local search.
Journal Article

Fundamentals of Scatter Search and Path Relinking

TL;DR: The features of Scatter Search and Path Relinking are described, which set them apart from other evolutionary approaches, and that offer opportunities for creating increasingly more versatile and effective methods in the future.
Journal ArticleDOI

Genetic fuzzy systems: taxonomy, current research trends and prospects

TL;DR: An overview of the field of GFSs, with a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process, and some potential future research directions.
Journal ArticleDOI

Design of wind farm layout for maximum wind energy capture

TL;DR: In this article, a model for wind turbine placement based on the wind distribution is presented to maximize the wind energy capture, which considers wake loss, which can be calculated based on wind turbine locations, and wind direction.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book

Genetic Programming: On the Programming of Computers by Means of Natural Selection

TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Book

Genetic Algorithms + Data Structures = Evolution Programs

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book

The Origins of Order: Self-Organization and Selection in Evolution

TL;DR: The structure of rugged fitness landscapes and the structure of adaptive landscapes underlying protein evolution, and the architecture of genetic regulatory circuits and its evolution.