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Information storage and retrieval systems. --- Computer science. --- Informatics --- Science --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data centers --- Digital libraries --- Information organization --- Information retrieval --- Intel·ligència artificial

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This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics.   The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical parallelism, and best practices in using and evaluating them. A second part deals with the theory for pGAs, with an eye on theory-to-practice issues. A final third part offers a very wide study of pGAs as practical problem solvers, addressing domains such as natural language processing, circuits design, scheduling, and genomics.   This volume will be helpful both for researchers and practitioners. The first part shows pGAs to either beginners and mature researchers looking for a unified view of the two fields: GAs and parallelism. The second part partially solves (and also opens) new investigation lines in theory of pGAs. The third part can be accessed independently for readers interested in applications. The result is an excellent source of information on the state of the art and future developments in parallel GAs.    .

Genetic algorithms --- Artificial intelligence --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Applied Mathematics --- Operations Research --- Civil Engineering --- Genetic algorithms. --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- 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 --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Mathematical and Computational Engineering. --- Artificial Intelligence.

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Operational research. Game theory --- Planning (firm) --- Production management --- Computer. Automation --- automatisering --- informatica --- mathematische modellen --- speltheorie --- wiskunde --- productieorganisatie --- logistiek --- operationeel onderzoek

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Maritime law

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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.