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Evolutionary Algorithms for Solving Multi-Objective Problems
This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
Applications of Multi-objective Evolutionary Algorithms
- Detailed MOEA applications discussed by international experts - State-of-the-art practical insights in tackling statistical optimization with MOEAs - A unique monograph covering a wide spectrum of real-world applications - Step-by-step discussion of MOEA applications in a variety of domains
Evolutionary Scheduling
Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
Evolutionary Multi-Criterion Optimization
This book constitutes the refereed proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2007, held in Matsushima, Japan in March 2007. The 65 revised full papers presented together with 4 invited papers are organized in topical sections on algorithm design, algorithm improvements, alternative methods, applications, engineering design, many objectives, objective handling, and performance assessments.
Evolutionary Computation in Combinatorial Optimization
This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2005, held in Lausanne, Switzerland in March/April 2005. The 24 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers cover evolutionary algorithms as well as related approaches like scatter search, simulated annealing, ant colony optimization, immune algorithms, variable neighborhood search, hyperheuristics, and estimation of distribution algorithms. The papers deal with representations, analysis of operators and fitness landscapes, and comparison algorithms. Among the combinatorial optimization problems studied are graph coloring, quadratic assignment, knapsack, graph matching, packing, scheduling, timetabling, lot-sizing, and the traveling salesman problem.
Parallel Computing for Bioinformatics and Computational Biology
Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed comp...
Evolutionary Multi-Criterion Optimization
This book constitutes the refereed proceedings of the First International Conference on Multi-Criterion Optimization, EMO 2001, held in Zurich, Switzerland in March 2001. The 45 revised full papers presented were carefully reviewed and selected from a total of 87 submissions. Also included are two tutorial surveys and two invited papers. The book is organized in topical sections on algorithm improvements, performance assessment and comparison, constraint handling and problem decomposition, uncertainty and noise, hybrid and alternative methods, scheduling, and applications of multi-objective optimization in a variety of fields.
Knowledge-Based Intelligent Information and Engineering Systems
2.1 Text Summarization “Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks)” [3]. Basic and classical articles in text summarization appear in “Advances in automatic text summarization” [3]. A literature survey on information extraction and text summarization is given by Zechner [7]. In general, the process of automatic text summarization is divided into three stages: (1) analysis of the given text, (2) summarization of the text, (3) presentation of the summary in a suitable output form. Titles, abstracts and keywords are the most common summaries ...
