Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance. The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently. The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

Genetic programming (Computer science) --- Genetic algorithms. --- Evolutionary programming (Computer science) --- Representations of groups. --- Representations of algebras. --- Algebra --- Group representation (Mathematics) --- Groups, Representation theory of --- Group theory --- Computer programming --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Learning classifier systems --- Genetic algorithms --- Engineering mathematics. --- Artificial intelligence. --- Operations research. --- Information technology. --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Operations Research/Decision Theory. --- IT in Business. --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- 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 --- Applied mathematics. --- Decision making. --- Business—Data processing. --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Decision making

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Most textbooks on modern heuristics provide the reader with detailed descriptions of the functionality of single examples like genetic algorithms, genetic programming, tabu search, simulated annealing, and others, but fail to teach the underlying concepts behind these different approaches. The author takes a different approach in this textbook by focusing on the users' needs and answering three fundamental questions: First, he tells us which problems modern heuristics are expected to perform well on, and which should be left to traditional optimization methods. Second, he teaches us to systematically design the "right" modern heuristic for a particular problem by providing a coherent view on design elements and working principles. Third, he shows how we can make use of problem-specific knowledge for the design of efficient and effective modern heuristics that solve not only small toy problems but also perform well on large real-world problems. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.

Heuristic algorithms. --- Heuristic programming. --- Heuristic programming --- Combinatorial optimization --- Civil & Environmental Engineering --- Mechanical Engineering --- Engineering & Applied Sciences --- Operations Research --- Computer Science --- Mechanical Engineering - General --- Information Technology --- Artificial Intelligence --- Heuristics (Computer algorithms) --- Computer science. --- Information technology. --- Business --- Artificial intelligence. --- Mathematical optimization. --- Computational intelligence. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Optimization. --- Computational Intelligence. --- IT in Business. --- Data processing. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System 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 --- Fifth generation computers --- Neural computers --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Informatics --- Science --- Programming (Mathematics) --- Computer algorithms --- Engineering. --- Artificial Intelligence. --- Construction --- Industrial arts --- Combinatorial optimization. --- Business—Data processing. --- Business information services. --- Business enterprises --- Information services

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- neuronale netwerken --- fuzzy logic --- cybernetica --- bedrijfseconomie --- automatisering --- KI (kunstmatige intelligentie) --- informatica management --- robots --- AI (artificiële intelligentie)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The book at hand presents a careful selection of relevant applications of CI methods for transport, logistics, and supply chain management problems. The chapters illustrate the current state-of-the-art in the application of CI methods in these fields and should help and inspire researchers and practitioners to apply and develop efficient methods. A few contributions in this book are extended versions of papers presented at EvoTransLog2007: The First European Workshop on Evolutionary Computation in Transportation and Logistics which was held in Valencia, Spain, in 2007. The majority of contributions are from additional, specially selected researchers, who have done relevant work in different areas of transport, logistics, and supply chain management. The goal is to broadly cover representative applications in these fields as well as different types of solution approaches. On the application side, the contributions focus on design of traffic and transportation networks, vehicle routing, and other important aspects of supply chain management such as inventory management, lot sizing, and lot scheduling. On the method side, the contributions deal with evolutionary algorithms, local search approaches, and scatter search combined with other CI techniques such as neural networks or fuzzy approaches. The book is structured according to the application domains. Thus, it has three parts dealing with traffic and transportation networks, vehicle routing, and supply chain management.

Engineering. --- Software engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Automotive engineering. --- Engineering economics. --- Engineering economy. --- Automotive Engineering. --- Artificial Intelligence (incl. Robotics). --- Appl.Mathematics/Computational Methods of Engineering. --- Software Engineering. --- Engineering Economics, Organization, Logistics, Marketing. --- Economy, Engineering --- Engineering economics --- Industrial engineering --- 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 --- Computer software engineering --- Construction --- Industrial arts --- Technology --- Mathematics --- Business logistics. --- Computational intelligence. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Supply chain management --- Industrial management --- Logistics --- Artificial Intelligence. --- Mathematical and Computational Engineering. --- Transportation. --- Public transportation --- Transport --- Transportation --- Transportation, Primitive --- Transportation companies --- Transportation industry --- Locomotion --- Commerce --- Communication and traffic --- Storage and moving trade --- Economic aspects

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Most textbooks on modern heuristics provide the reader with detailed descriptions of the functionality of single examples like genetic algorithms, genetic programming, tabu search, simulated annealing, and others, but fail to teach the underlying concepts behind these different approaches. The author takes a different approach in this textbook by focusing on the users' needs and answering three fundamental questions: First, he tells us which problems modern heuristics are expected to perform well on, and which should be left to traditional optimization methods. Second, he teaches us to systematically design the "right" modern heuristic for a particular problem by providing a coherent view on design elements and working principles. Third, he shows how we can make use of problem-specific knowledge for the design of efficient and effective modern heuristics that solve not only small toy problems but also perform well on large real-world problems. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- neuronale netwerken --- fuzzy logic --- cybernetica --- bedrijfseconomie --- automatisering --- KI (kunstmatige intelligentie) --- informatica management --- robots