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The series is designed to bring together those mathematicians who are seriously interested in getting new challenging stimuli from economic theories with those economists who are seeking effective mathematical tools for their research. A lot of economic problems can be formulated as constrained optimizations and equilibration of their solutions. Various mathematical theories have been supplying economists with indispensable machineries for these problems arising in economic theory. Conversely, mathematicians have been stimulated by various mathematical difficulties raised by economic theories.

Mathematics. --- Game theory. --- Probabilities. --- Game Theory, Economics, Social and Behav. Sciences. --- Probability Theory and Stochastic Processes. --- Economics, Mathematical. --- Economics --- Mathematical economics --- Mathematics --- Econometrics --- Methodology --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Math --- Science --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Games, Theory of --- Theory of games --- Mathematical models

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This book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion – state-of-the-art approaches for the application of computational intelligence to engineering problems. The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from seven countries – some of which are the original proponents of the methods presented – and 21 research centers around the globe.

Computational intelligence. --- Artificial intelligence --- Engineering applications. --- Engineering. --- Distribution (Probability theory. --- Computational Intelligence. --- Discrete Optimization. --- Continuous Optimization. --- Probability Theory and Stochastic Processes. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Construction --- Industrial arts --- Technology --- Mathematical optimization. --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Intelligence, Computational --- Soft computing

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This book combines a model reduction technique with an efficient parametrization scheme for the purpose of solving a class of complex and computationally expensive simulation-based problems involving finite element models. These problems, which have a wide range of important applications in several engineering fields, include reliability analysis, structural dynamic simulation, sensitivity analysis, reliability-based design optimization, Bayesian model validation, uncertainty quantification and propagation, etc. The solution of this type of problems requires a large number of dynamic re-analyses. To cope with this difficulty, a model reduction technique known as substructure coupling for dynamic analysis is considered. While the use of reduced order models alleviates part of the computational effort, their repetitive generation during the simulation processes can be computational expensive due to the substantial computational overhead that arises at the substructure level. In this regard, an efficient finite element model parametrization scheme is considered. When the division of the structural model is guided by such a parametrization scheme, the generation of a small number of reduced order models is sufficient to run the large number of dynamic re-analyses. Thus, a drastic reduction in computational effort is achieved without compromising the accuracy of the results. The capabilities of the developed procedures are demonstrated in a number of simulation-based problems involving uncertainty.

Simulation methods. --- Simulation techniques --- System simulation --- Operations research --- Systems engineering --- Models and modelmaking --- Mechanics. --- Mechanics, Applied. --- Computer science. --- Distribution (Probability theory. --- Solid Mechanics. --- Computational Science and Engineering. --- Probability Theory and Stochastic Processes. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Informatics --- Science --- Applied mechanics --- Engineering, Mechanical --- Engineering mathematics --- Classical mechanics --- Newtonian mechanics --- Physics --- Dynamics --- Quantum theory --- Computer mathematics. --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Computer mathematics --- Electronic data processing --- Solids. --- Probability Theory. --- Data processing. --- Solid state physics --- Transparent solids

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This book comprises nine selected works on numerical and computational methods for solving multiobjective optimization, game theory, and machine learning problems. It provides extended versions of selected papers from various fields of science such as computer science, mathematics and engineering that were presented at EVOLVE 2013 held in July 2013 at Leiden University in the Netherlands. The internationally peer-reviewed papers include original work on important topics in both theory and applications, such as the role of diversity in optimization, statistical approaches to combinatorial optimization, computational game theory, and cell mapping techniques for numerical landscape exploration. Applications focus on aspects including robustness, handling multiple objectives, and complex search spaces in engineering design and computational biology.

Engineering. --- Computer science --- Artificial intelligence. --- Computer mathematics. --- Probabilities. --- Computational intelligence. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Probability Theory and Stochastic Processes. --- Mathematics of Computing. --- Computational Mathematics and Numerical Analysis. --- Mathematics. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Distribution (Probability theory. --- Computer science. --- Artificial Intelligence. --- Informatics --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Evolutionary computation --- Computer science—Mathematics.

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This book provides new theories, applications and quantitative methods in demography, population studies and statistics. It presents and applies data analysis, statistics and stochastic modeling techniques focusing on demography, population aging, mortality and health sciences. The book describes diverse stochastic processes as well as Markov and semi-Markov models in demography and population studies, along with chapters on statistical models and methods in biostatistics and epidemiology. As such the book will be a valuable source to demographers, health scientists, statisticians, economists and sociologists.

Social sciences. --- Public health. --- Probabilities. --- Statistics. --- Demography. --- Social Sciences. --- Public Health. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Probability Theory and Stochastic Processes. --- Historical demography --- Social sciences --- Population --- Vital statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Community health --- Health services --- Hygiene, Public --- Hygiene, Social --- Public health services --- Public hygiene --- Sanitary affairs --- Social hygiene --- Health --- Human services --- Biosecurity --- Health literacy --- Medicine, Preventive --- National health services --- Sanitation --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Population aging. --- Aging of population --- Aging population --- Aging society --- Demographic aging --- Graying (Demography) --- Greying (Demography) --- Age distribution (Demography) --- Distribution (Probability theory. --- Statistics for Social Sciences, Humanities, Law. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistics .