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Evolutionary computation --- Evolutionary programming (Computer science) --- Swarm intelligence --- Evolutionary computation. --- Swarm intelligence. --- Collective intelligence --- Computation, Evolutionary --- Cellular automata --- Distributed artificial intelligence --- Computer programming --- Neural networks (Computer science) --- Information Technology --- Computational Biosciences --- Modelling & Simulation --- Réseaux neuronaux à structure évolutive --- Programmation évolutive

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Neural networks (Computer science) --- Evolutionary computation --- Réseaux neuronaux (Informatique) --- Réseaux neuronaux à structure évolutive --- Algorithmes génétiques --- Programmation évolutive --- Programmation génétique (Informatique) --- Evolutionary computation. --- Neurale netwerken. --- Algoritmen. --- Computermethoden. --- Zoekstrategieën. --- Programmeren (computers) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Computation, Evolutionary --- Périodiques. --- Genetic algorithms --- Evolutionary programming (Computer science) --- Genetic programming (Computer science) --- Periodicals. --- Computer programming --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Learning classifier systems --- Genetic algorithms.

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This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2006, held in Budapest, Hungary in April 2006. The 24 revised full papers presented were carefully reviewed and selected from 77 submissions. The papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, greedy randomized adaptive search procedures, ant colony optimization, and particle swarm optimization algorithms. The papers deal with representations, heuristics, analysis of problem structures, and comparisons of algorithms. The list of studied combinatorial optimization problems includes prominent examples like graph coloring, knapsack problems, the traveling salesperson problem, scheduling, graph matching, as well as specific real-world problems.

Evolutionary computation --- Evolutionary programming (Computer science) --- Combinatorial optimization --- Réseaux neuronaux à structure évolutive --- Programmation évolutive --- Optimisation combinatoire --- Congresses. --- Congrès --- Computer Science --- Engineering & Applied Sciences --- Computer science. --- Computers. --- Algorithms. --- Numerical analysis. --- Computer science --- Computer Science. --- Computation by Abstract Devices. --- Algorithm Analysis and Problem Complexity. --- Numeric Computing. --- Discrete Mathematics in Computer Science. --- Mathematics. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Mathematical analysis --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Informatics --- Science --- Mathematics --- Foundations --- Computer software. --- Electronic data processing. --- Computational complexity. --- Complexity, Computational --- ADP (Data processing) --- Automatic data processing --- Data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Computers --- Office practice --- Software, Computer --- Automation --- Computer science—Mathematics. --- Discrete mathematics. --- Theory of Computation. --- Numerical Analysis. --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis

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This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel.

Genetic algorithms --- Evolutionary computation. --- Combinatorial optimization. --- Réseaux neuronaux à structure évolutive --- Optimisation combinatoire --- Mathematical models. --- Data processing. --- Genetic algorithms -- Data processing. --- Genetic algorithms -- Mathematical models. --- Combinatorial optimization, --- Evolutionary computation --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Operations Research --- Civil Engineering --- Computer Science --- Applied Mathematics --- Data processing --- Optimization, Combinatorial --- Computation, Evolutionary --- 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 --- Combinatorial analysis --- Mathematical optimization --- Neural networks (Computer science) --- Algorithms --- Combinatorial optimization --- Genetic programming (Computer science) --- Learning classifier systems --- Mathematical and Computational Engineering. --- Artificial Intelligence.

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One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

Evolutionary computation --- Genetic algorithms --- Réseaux neuronaux à structure évolutive --- Algorithmes génétiques --- Electronic books. -- local. --- Evolutionary computation -- Congresses. --- Genetic algorithms -- Congresses. --- Operations Research --- Applied Mathematics --- Civil Engineering --- Computer Science --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Evolutionary computation. --- Genetic algorithms. --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- 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 --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics --- Computation, Evolutionary --- Neural networks (Computer science) --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Genetic programming (Computer science) --- Learning classifier systems