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Metaheuristic Procedures For Training Neural Networks provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Apart from Chapter 1, which reviews classical training methods, the chapters are divided into three main categories. The first one is devoted to local search based methods, including Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. The third part covers other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. Overall, the book's objective is engineered to provide a broad coverage of the concepts, methods, and tools of this important area of ANNs within the realm of continuous optimization.

Neural networks (Computer science) --- Heuristic programming. --- Mathematical optimization. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Artificial intelligence --- Programming (Mathematics) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation --- Soft computing --- Operations research. --- Production management. --- Computer science --- Operations Research/Decision Theory. --- Optimization. --- Mathematical Modeling and Industrial Mathematics. --- Operations Research, Management Science. --- Operations Management. --- Computational Mathematics and Numerical Analysis. --- Mathematics. --- Manufacturing management --- Industrial management --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Mathematics --- Decision making. --- Mathematical models. --- Management science. --- Computer mathematics. --- Quantitative business analysis --- Management --- Problem solving --- Statistical decision --- Models, Mathematical --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Decision making --- Mathematics—Data processing. --- Operations Research and Decision Theory. --- Operations Research, Management Science .

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CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability. The methods are benchmarked against well-known metaheutistics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.

Economics/Management Science. --- Mathematical Modeling and Industrial Mathematics. --- Optimization. --- Production/Logistics. --- Algorithms. --- Genetics and Population Dynamics. --- Operations Research/Decision Theory. --- Economics. --- Genetics --- Mathematical optimization. --- Business logistics. --- Economie politique --- Algorithmes --- Optimisation mathématique --- Logistique (Organisation) --- Mathematics. --- Genetic algorithms. --- Nonlinear programming. --- Operations Research --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Evolutionary programming (Computer science) --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Production management. --- Operations research. --- Decision making. --- Numerical analysis. --- Biomathematics. --- Numerical Analysis. --- Operation Research/Decision Theory. --- Operations Management. --- Programming (Mathematics) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Computer programming --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Manufacturing management --- Industrial management --- Algorism --- Algebra --- Arithmetic --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Foundations --- Mathematics --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Decision making --- Population genetics. --- Operations Research and Decision Theory. --- Population Genetics.

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This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools to this end, and then we can find in the book how to best use genetic algorithms, particle swarm, ant colonies, immune systems, variable neighborhood search, and many other bioinspired techniques. Also, neural network solutions are considered in this book. Both, theory and practice have been addressed in the chapters of the book. Mathematical background and methodological tools in solving this new class of problems and applications are included. From the applications point of view, not just academic benchmarks are dealt with, but also real world applications in logistics and bioinformatics are discussed here. The book then covers theory and practice, as well as discrete versus continuous dynamic optimization, in the aim of creating a fresh and comprehensive volume. This book is targeted to either beginners and experienced practitioners in dynamic  optimization, since we took care of devising the chapters in a way that a wide audience could profit from its contents. We hope to offer a single source for up-to-date information in dynamic optimization, an inspiring and attractive new research domain that appeared in these last years and is here to stay.

Engineering & Applied Sciences --- Computer Science --- Combinatorial optimization. --- Computer algorithms. --- Computational intelligence. --- Intelligence, Computational --- Optimization, Combinatorial --- Engineering. --- Artificial intelligence. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Artificial intelligence --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Algorithms --- Combinatorial analysis --- Mathematical optimization --- Artificial Intelligence.

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"Parallel Evolutionary Computation" focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications. The book is divided into four parts. The first part deals with a clear software-like and algorithmic vision on parallel evolutionary optimizations. The second part is about hardware implementations of genetic algorithms, a valuable topic which is hard to find in the present literature. The third part treats the problem of distributed evolutionary computation and presents three interesting applications wherein parallel EC new ideas are featured. Finally, the last part deals with the up-to-date field of parallel particle swarm optimization to illustrate the intrinsic similarities and potential extensions to techniques in this domain. The book offers a wide spectrum of sample works developed in leading research throughout the world about parallel implementations of efficient techniques at the heart of computational intelligence. It will be useful both for beginners and experienced researchers in the field of computational intelligence.

Evolutionary computation. --- Parallel processing (Electronic computers) --- Réseaux neuronaux à structure évolutive --- Parallélisme (Informatique) --- Engineering. --- Artificial intelligence. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Evolutionary computation --- Computer Science --- Applied Mathematics --- Civil Engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Computation, Evolutionary --- Applied mathematics. --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Mathematics --- High performance computing --- Multiprocessors --- Parallel programming (Computer science) --- Supercomputers --- Neural networks (Computer science) --- Mathematical and Computational Engineering. --- Artificial Intelligence.

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This is the proceedings of the "8th IMACS Seminar on Monte Carlo Methods" held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures. The history of the IMACS Seminar on Monte Carlo Methods goes back to April 1997 when the first MCM Seminar was organized in Brussels: 1st IMACS Seminar, 1997, Brussels, Belgium2nd IMACS Seminar, 1999, Varna, Bulgaria 3rd IMACS Seminar, 2001, Salzburg, Austria 4th IMACS Seminar, 2003, Berlin, Germany 5th IMACS Seminar, 2005, Tallahassee, USA6th IMACS Seminar, 2007, Reading, UK7th IMACS Seminar, 2009, Brussels, Belgium8th IMACS Seminar, 2011, Borovets, Bulgaria

Monte Carlo method --- Mathematics --- Math --- Science --- Monte Carlo Method, Stochastic Model, Financial Mathematics.