Scalable Optimization Via Probabilistic Modeling

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Scalable Optimization via Probabilistic Modeling

Martin Pelikan,Kumara Sastry,Erick Cantú-Paz
Scalable Optimization via Probabilistic Modeling Book PDF

Author : Martin Pelikan,Kumara Sastry,Erick Cantú-Paz
Publisher : Springer
Page : 349 pages
File Size : 48,9 Mb
Release : 2007-01-12
Category : Mathematics
ISBN : 9783540349549

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Scalable Optimization via Probabilistic Modeling by Martin Pelikan,Kumara Sastry,Erick Cantú-Paz Pdf

I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.

Clever Algorithms

Jason Brownlee
Clever Algorithms Book PDF

Author : Jason Brownlee
Publisher : Jason Brownlee
Page : 437 pages
File Size : 51,5 Mb
Release : 2011
Category : Computers
ISBN : 9781446785065

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Clever Algorithms by Jason Brownlee Pdf

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.

Parallel Problem Solving from Nature - PPSN X

Günter Rudolph,Thomas Jansen,Simon M. Lucas,Carlo Poloni,Nicola Beume
Parallel Problem Solving from Nature PPSN X Book PDF

Author : Günter Rudolph,Thomas Jansen,Simon M. Lucas,Carlo Poloni,Nicola Beume
Publisher : Springer
Page : 1183 pages
File Size : 44,6 Mb
Release : 2008-09-16
Category : Computers
ISBN : 9783540877004

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Parallel Problem Solving from Nature - PPSN X by Günter Rudolph,Thomas Jansen,Simon M. Lucas,Carlo Poloni,Nicola Beume Pdf

This book constitutes the refereed proceedings of the 10th International Conference on Parallel Problem Solving from Nature, PPSN 2008, held in Dortmund, Germany, in September 2008. The 114 revised full papers presented were carefully reviewed and selected from 206 submissions. The conference covers a wide range of topics, such as evolutionary computation, quantum computation, molecular computation, neural computation, artificial life, swarm intelligence, artificial ant systems, artificial immune systems, self-organizing systems, emergent behaviors, and applications to real-world problems. The paper are organized in topical sections on formal theory, new techniques, experimental analysis, multiobjective optimization, hybrid methods, and applications.

Computational Intelligence in Expensive Optimization Problems

Yoel Tenne,Chi-Keong Goh
Computational Intelligence in Expensive Optimization Problems Book PDF

Author : Yoel Tenne,Chi-Keong Goh
Publisher : Springer Science & Business Media
Page : 800 pages
File Size : 44,9 Mb
Release : 2010-03-10
Category : Technology & Engineering
ISBN : 9783642107016

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Computational Intelligence in Expensive Optimization Problems by Yoel Tenne,Chi-Keong Goh Pdf

In modern science and engineering, laboratory experiments are replaced by high fidelity and computationally expensive simulations. Using such simulations reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc. Under such difficulties, classical optimization and analysis methods may perform poorly. This motivates the application of computational intelligence methods such as evolutionary algorithms, neural networks and fuzzy logic, which often perform well in such settings. This is the first book to introduce the emerging field of computational intelligence in expensive optimization problems. Topics covered include: dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic. reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporation of expert systems and human-system interface, single and multiobjective algorithms, data mining and statistical analysis, analysis of real-world cases (such as multidisciplinary design optimization). The edited book provides both theoretical treatments and real-world insights gained by experience, all contributed by leading researchers in the respective fields. As such, it is a comprehensive reference for researchers, practitioners, and advanced-level students interested in both the theory and practice of using computational intelligence for expensive optimization problems.

Multiobjective Problem Solving from Nature

Joshua Knowles,David Corne,Kalyanmoy Deb
Multiobjective Problem Solving from Nature Book PDF

Author : Joshua Knowles,David Corne,Kalyanmoy Deb
Publisher : Springer Science & Business Media
Page : 413 pages
File Size : 44,5 Mb
Release : 2008-01-28
Category : Computers
ISBN : 9783540729631

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Multiobjective Problem Solving from Nature by Joshua Knowles,David Corne,Kalyanmoy Deb Pdf

This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concept of multiobjective optimization can be used to reformulate and resolve problems in areas such as constrained optimization, co-evolution, classification, inverse modeling, and design.

Springer Handbook of Computational Intelligence

Janusz Kacprzyk,Witold Pedrycz
Springer Handbook of Computational Intelligence Book PDF

Author : Janusz Kacprzyk,Witold Pedrycz
Publisher : Springer
Page : 1634 pages
File Size : 49,9 Mb
Release : 2015-05-28
Category : Technology & Engineering
ISBN : 9783662435052

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Springer Handbook of Computational Intelligence by Janusz Kacprzyk,Witold Pedrycz Pdf

The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.

Learning Classifier Systems

Jaume Bacardit,Ester Bernadó-Mansilla
Learning Classifier Systems Book PDF

Author : Jaume Bacardit,Ester Bernadó-Mansilla
Publisher : Springer Science & Business Media
Page : 316 pages
File Size : 50,7 Mb
Release : 2008-10-23
Category : Computers
ISBN : 9783540881377

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Learning Classifier Systems by Jaume Bacardit,Ester Bernadó-Mansilla Pdf

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

Evolutionary Multi-Criterion Optimization

Ricardo H.C. Takahashi,Kalyanmoy Deb,Elizabeth F. Wanner,Salvatore Greco
Evolutionary Multi Criterion Optimization Book PDF

Author : Ricardo H.C. Takahashi,Kalyanmoy Deb,Elizabeth F. Wanner,Salvatore Greco
Publisher : Springer
Page : 620 pages
File Size : 42,5 Mb
Release : 2011-03-25
Category : Computers
ISBN : 9783642198939

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Evolutionary Multi-Criterion Optimization by Ricardo H.C. Takahashi,Kalyanmoy Deb,Elizabeth F. Wanner,Salvatore Greco Pdf

This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the development of algorithmically efficient tools for the evaluation of solution-set quality , the theoretical questions related to solution archiving and others. They report on the continuing effort in the development of algorithms, either for dealing with particular classes of problems or for new forms of processing the problem information. Almost one third of the papers is related to EMO applications in a diversity of fields. Eleven papers are devoted to promote the interaction with the related field of Multi-Criterion Decision Making (MCDM).

Evolutionary Optimization Algorithms

Dan Simon
Evolutionary Optimization Algorithms Book PDF

Author : Dan Simon
Publisher : John Wiley & Sons
Page : 776 pages
File Size : 48,6 Mb
Release : 2013-06-13
Category : Mathematics
ISBN : 9781118659502

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Evolutionary Optimization Algorithms by Dan Simon Pdf

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Linkage in Evolutionary Computation

Ying-ping Chen
Linkage in Evolutionary Computation Book PDF

Author : Ying-ping Chen
Publisher : Springer
Page : 488 pages
File Size : 47,5 Mb
Release : 2008-09-10
Category : Computers
ISBN : 9783540850687

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Linkage in Evolutionary Computation by Ying-ping Chen Pdf

In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily ”fooled” by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.

Hierarchical Bayesian Optimization Algorithm

Martin Pelikan
Hierarchical Bayesian Optimization Algorithm Book PDF

Author : Martin Pelikan
Publisher : Springer Science & Business Media
Page : 194 pages
File Size : 55,6 Mb
Release : 2005-02
Category : Computers
ISBN : 3540237747

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Hierarchical Bayesian Optimization Algorithm by Martin Pelikan Pdf

This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope. The algorithms are also extensively tested on two interesting classes of real-world problems: MAXSAT and Ising spin glasses with periodic boundary conditions in two and three dimensions. Experimental results validate the theoretical model and confirm that BOA and hBOA provide robust and scalable solution for nearly decomposable and hierarchical problems with only little problem-specific information.

Handbook of Optimization

Ivan Zelinka,Václav Snásel,Ajith Abraham
Handbook of Optimization Book PDF

Author : Ivan Zelinka,Václav Snásel,Ajith Abraham
Publisher : Springer Science & Business Media
Page : 1088 pages
File Size : 55,9 Mb
Release : 2012-08-13
Category : Computers
ISBN : 9783642305030

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Handbook of Optimization by Ivan Zelinka,Václav Snásel,Ajith Abraham Pdf

Optimization problems were and still are the focus of mathematics from antiquity to the present. Since the beginning of our civilization, the human race has had to confront numerous technological challenges, such as finding the optimal solution of various problems including control technologies, power sources construction, applications in economy, mechanical engineering and energy distribution amongst others. These examples encompass both ancient as well as modern technologies like the first electrical energy distribution network in USA etc. Some of the key principles formulated in the middle ages were done by Johannes Kepler (Problem of the wine barrels), Johan Bernoulli (brachystochrone problem), Leonhard Euler (Calculus of Variations), Lagrange (Principle multipliers), that were formulated primarily in the ancient world and are of a geometric nature. In the beginning of the modern era, works of L.V. Kantorovich and G.B. Dantzig (so-called linear programming) can be considered amongst others. This book discusses a wide spectrum of optimization methods from classical to modern, alike heuristics. Novel as well as classical techniques is also discussed in this book, including its mutual intersection. Together with many interesting chapters, a reader will also encounter various methods used for proposed optimization approaches, such as game theory and evolutionary algorithms or modelling of evolutionary algorithm dynamics like complex networks.

Success in Evolutionary Computation

Ang Yang,Yin Shan,Lam Thu Bui
Success in Evolutionary Computation Book PDF

Author : Ang Yang,Yin Shan,Lam Thu Bui
Publisher : Springer
Page : 372 pages
File Size : 53,7 Mb
Release : 2008-03-22
Category : Technology & Engineering
ISBN : 9783540762867

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Success in Evolutionary Computation by Ang Yang,Yin Shan,Lam Thu Bui Pdf

Evolutionary Computation (EC) includes a number of techniques such as Genetic Algorithms which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting and rapidly-evolving field.

Parallel Problem Solving from Nature - PPSN XII

Carlos Coello Coello,Vincenzo Cutello,Kalyanmoy Deb,Stephanie Forrest,Giuseppe Nicosia,Mario Pavone
Parallel Problem Solving from Nature PPSN XII Book PDF

Author : Carlos Coello Coello,Vincenzo Cutello,Kalyanmoy Deb,Stephanie Forrest,Giuseppe Nicosia,Mario Pavone
Publisher : Springer
Page : 541 pages
File Size : 50,7 Mb
Release : 2012-08-27
Category : Computers
ISBN : 9783642329371

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Parallel Problem Solving from Nature - PPSN XII by Carlos Coello Coello,Vincenzo Cutello,Kalyanmoy Deb,Stephanie Forrest,Giuseppe Nicosia,Mario Pavone Pdf

The two volume set LNCS 7491 and 7492 constitutes the refereed proceedings of the 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012, held in Taormina, Sicily, Italy, in September 2012. The total of 105 revised full papers were carefully reviewed and selected from 226 submissions. The meeting began with 5 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN 2012 also included 8 tutorials. The papers are organized in topical sections on evolutionary computation; machine learning, classifier systems, image processing; experimental analysis, encoding, EDA, GP; multiobjective optimization; swarm intelligence, collective behavior, coevolution and robotics; memetic algorithms, hybridized techniques, meta and hyperheuristics; and applications.

Hybrid Artificial Intelligence Systems

Emilio Corchado,Xindong Wu,Erkki Oja,Bruno Baruque
Hybrid Artificial Intelligence Systems Book PDF

Author : Emilio Corchado,Xindong Wu,Erkki Oja,Bruno Baruque
Publisher : Springer Science & Business Media
Page : 736 pages
File Size : 49,5 Mb
Release : 2009-06-02
Category : Computers
ISBN : 9783642023187

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Hybrid Artificial Intelligence Systems by Emilio Corchado,Xindong Wu,Erkki Oja,Bruno Baruque Pdf

This volume constitutes the refereed proceedings of the 4th International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2009, held in Salamanca, Spain, in June 2009. The 85 papers presented, were carefully reviewed and selected from 206 submissions. The topics covered are agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, real world HAIS applications and data uncertainty, hybrid artificial intelligence in bioinformatics, evolutionary multiobjective machine learning, hybrid reasoning and coordination methods on multi-agent systems, methods of classifiers fusion, knowledge extraction based on evolutionary learning, hybrid systems based on bioinspired algorithms and argumentation methods, hybrid evolutionry intelligence in financial engineering.