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Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Evolutionary computation. --- Evolutionary programming (Computer science). --- Genetic algorithms. --- Evolutionary programming (Computer science) --- Genetic algorithms --- Evolutionary computation --- Engineering & Applied Sciences --- Computer Science --- Computation, Evolutionary --- Artificial intelligence. --- Data structures (Computer scienc. --- Engineering. --- Computer simulation. --- Artificial Intelligence (incl. Robotics). --- Data Structures. --- Complexity. --- Control, Robotics, Mechatronics. --- Simulation and Modeling. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Construction --- Industrial arts --- Technology --- 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 --- Fifth generation computers --- Neural computers --- Neural networks (Computer science) --- Artificial Intelligence. --- Data structures (Computer science). --- Computational complexity. --- Control engineering. --- Robotics. --- Mechatronics. --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Automation --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Programmable controllers --- Complexity, Computational --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science)

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Network models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems.

Industrial engineering --- Genetic algorithms. --- Mathematical models. --- Engineering economy. --- Combinatorics. --- Operations research. --- Computer software. --- Engineering Economics, Organization, Logistics, Marketing. --- Operations Research/Decision Theory. --- Algorithm Analysis and Problem Complexity. --- Software, Computer --- Computer systems --- Operational analysis --- Operational research --- Management science --- Research --- System theory --- Combinatorics --- Algebra --- Mathematical analysis --- Economy, Engineering --- Engineering economics --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Engineering economics. --- Decision making. --- Algorithms. --- Algorism --- Arithmetic --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Foundations --- Decision making

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Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: ¢ genetic algorithms, ¢ differential evolution, ¢ swarm intelligence, and ¢ artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Electrical engineering --- Applied physical engineering --- Engineering sciences. Technology --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- mechatronica --- superclaus proces --- complexiteit --- industriële robots --- vormgeving --- automatisering --- informatica --- mineralen (chemie) --- simulaties --- mijnbouw --- database management --- programmatielogica --- KI (kunstmatige intelligentie) --- robots --- automatische regeltechniek

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This volume provides a complete record of presentations made at Industrial Engineering, Management Science and Applications 2015 (ICIMSA 2015), and provides the reader with a snapshot of current knowledge and state-of-the-art results in industrial engineering, management science and applications. The goal of ICIMSA is to provide an excellent international forum for researchers and practitioners from both academia and industry to share cutting-edge developments in the field, and to exchange and distribute the latest research and theories from the international community. The conference is held every year, making it an ideal platform for people to share their views and experiences in industrial engineering, management science and applications related fields.

Computer Science. --- Management of Computing and Information Systems. --- Engineering Economics, Organization, Logistics, Marketing. --- Operation Research/Decision Theory. --- Facility Management. --- Computer science. --- Information Systems. --- Engineering economy. --- Engineering. --- Operations research. --- Informatique --- Décision économique, prise de --- Ingénierie --- Recherche opérationnelle --- Engineering & Applied Sciences --- Computer Science --- Information storage and retrieval systems --- Systèmes d'information --- Industrial engineering --- Management science. --- Management. --- Quantitative business analysis --- Management engineering --- Simplification in industry --- Operations Research/Decision Theory. --- Construction --- Industrial arts --- Technology --- Operational analysis --- Operational research --- Management science --- Research --- System theory --- Economy, Engineering --- Engineering economics --- Management --- Problem solving --- Operations research --- Statistical decision --- Engineering --- Value analysis (Cost control) --- Management information systems. --- Engineering economics. --- Decision making. --- Facility management. --- Facilities management --- Factory management --- Plant engineering --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Informatics --- Science --- Computer-based information systems --- EIS (Information systems) --- Executive information systems --- MIS (Information systems) --- Sociotechnical systems --- Information resources management --- Decision making --- Communication systems

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This book is organized in 2 volumes and 6 parts. Part I is Big Data Analytics, which is about new advances of analysis, statistics, coordination  and data mining of big data; Part II is Information Systems Management, which is about the development of big data information system or cloud platform. Part III is Computing Methodology with Big Data, which is about the improvements of traditional computation technologies in the background of big data; Part IV is Uncertainty Decision Making, which is about the decision making methods with various uncertain information, such as fuzzy, random, rough, gray, unascertained. Part V is Intelligence Algorithm. Part VI is Data Security, which is a particularly important aspect in the modern management environment.

Engineering. --- Big data. --- Management information systems. --- Computer science. --- Industrial engineering. --- Production engineering. --- Industrial and Production Engineering. --- Big Data/Analytics. --- Management of Computing and Information Systems. --- Manufacturing engineering --- Process engineering --- Industrial engineering --- Mechanical engineering --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Informatics --- Science --- Computer-based information systems --- EIS (Information systems) --- Executive information systems --- MIS (Information systems) --- Sociotechnical systems --- Information resources management --- Management --- Data sets, Large --- Large data sets --- Construction --- Industrial arts --- Technology --- Communication systems --- Data sets --- Information Systems.