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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.

Combinatorial optimization. --- Computational intelligence. --- Computational intelligence --- Combinatorial optimization --- Computer Science --- Civil Engineering --- Applied Mathematics --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Optimization, Combinatorial --- Intelligence, Computational --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Applications of 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 --- Combinatorial analysis --- Mathematical optimization --- Artificial intelligence --- Soft computing

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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.

Mathematics --- Applied physical engineering --- Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- ICT (informatie- en communicatietechnieken) --- toegepaste wiskunde --- economie --- wiskunde --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- AI (artificiële intelligentie)

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The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design. This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.

Evolutionary computation --- Genetic algorithms --- Computer Science --- Operations Research --- Civil Engineering --- Applied Mathematics --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Computation, Evolutionary --- 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 --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Mehrkriterielle Optimierung --- Genetic algorithms. --- Evolutionary computation. --- Memetischer Algorithmus. --- Neural networks (Computer science) --- Algorithms --- Combinatorial optimization --- Genetic programming (Computer science) --- Learning classifier systems

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This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economics, Data mining, Machine learning, and Expert systems. "Computational Intelligence in Optimization" is a comprehensive reference for researchers, practitioners and advanced-level students interested in both the theory and practice of using computational intelligence in real-world applications.

Mathematics --- Applied physical engineering --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- neuronale netwerken --- fuzzy logic --- cybernetica --- toegepaste wiskunde --- automatisering --- economie --- wiskunde --- KI (kunstmatige intelligentie) --- robots

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Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots