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This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
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The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining. Such a collection intends to illustrate the potential of multi-objective swarm intelligence techniques in data mining, with the aim of motivating more researchers in evolutionary computation and machine learning to do research in this field. This volume consists of eleven chapters, including an introduction that provides the basic concepts of swarm intelligence techniques and a discussion of their use in data mining. Some of the research challenges that must be faced when using swarm intelligence techniques in data mining are also addressed. The rest of the chapters were contributed by leading researchers, and were organized according to the steps normally followed in Knowledge Discovery in Databases (KDD) (i.e., data preprocessing, data mining, and post processing). We hope that this book becomes a valuable reference for those wishing to do research on the use of multi-objective swarm intelligence techniques in data mining and knowledge discovery in databases.
Data mining --- Swarm intelligence --- Computational intelligence --- Applied Mathematics --- Civil Engineering --- Computer Science --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Swarm intelligence. --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Collective intelligence --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Database searching --- Cellular automata --- Distributed artificial intelligence --- 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
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The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems. Such a collection intends to provide an overview of the state-of-the-art developments in this field, with the aim of motivating more researchers in operations research, engineering, and computer science, to do research in this area. As such, this book is expected to become a valuable reference for those wishing to do research on the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems.
Natural computation --- Combinatorial optimization --- Computer science --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Applied Mathematics --- Civil Engineering --- Computer Science --- Mathematics --- Combinatorial optimization. --- Multidisciplinary design optimization. --- Design optimization, Multidisciplinary --- Multicriteria design optimization --- Multidisciplinary optimization (Engineering design) --- Optimization, Combinatorial --- 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 --- Engineering design --- Combinatorial analysis --- Mathematical optimization --- Mathematical and Computational Engineering. --- Artificial Intelligence.
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