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An Introduction to Genetic Algorithms
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An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.Abstract:
From the Publisher:
"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.
-- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.read more
Citations
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Journal ArticleDOI
A novel approach for optimal chiller loading using particle swarm optimization
TL;DR: Two new methods to solve optimal chiller loading (OCL) problem are employed, continuous genetic algorithm (GA) and particle swarm optimization (PSO), which easily overcome deficiencies in other conventional optimization methods.
Book ChapterDOI
Urban Energy Systems
Arnulf Grubler,Xuemei Bai,Thomas Buettner,Shobhakar Dhakal,David Fisk,Toshiaki Ichinose,James Keirstead,Gerd Sammer,David Satterthwaite,Niels Schulz,Nilay Shah,Julia K. Steinberger,Helga Weisz,Gilbert Ahamer,Tim Baynes,Daniel Curtis,Michael D. Doherty,Nick Eyre,Junichi Fujino,Keisuke Hanaki,Mikiko Kainuma,Shinji Kaneko,Manfred Lenzen,Jacqui Meyers,Hitomi Nakanishi,Victoria Novikova,Krishnan S. Rajan,Seongwon Seo,Ram M. Shrestha,Priyadarshi R. Shukla,Alice Sverdlik,Jayant Sathaye +31 more
TL;DR: The Global Environment Assessment (GEA) as mentioned in this paper estimates that between 60 and 80% of the final energy use globally is urban, with a central estimate of 75% to 80%.
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The Dow Theory: William Peter Hamilton's Track Record Reconsidered
TL;DR: Alfred Cowles' test of the Dow Theory appears to provide strong evidence against the ability of Wall Street's most famous chartist, William Peter Hamilton, to forecast the stock market.
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Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms
TL;DR: This work proposes an intelligent approach to build automatically an efficient and effective Deep Neural Network based anomaly Network IDS using a hybrid optimization framework (IGASAA) based on Improved Genetic Al algorithm (IGA) and Simulated Annealing Algorithm (SAA) and results are called “MLIDS”.
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Cross-Layer Cooperative MAC Protocol in Distributed Wireless Networks
TL;DR: Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance and shedding some light on the tradeoff between multi-user diversity gain at the physical layer and the helper contention overhead at the MAC layer.
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.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
The Evolution of Cooperation
TL;DR: In this paper, a model based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game was developed for cooperation in organisms, and the results of a computer tournament showed how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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.