Multiobjective Problem Solving from Nature

From Concepts to Applications

By: Joshua Knowles (Editor), David Corne (Editor), Kalyanmoy Deb (Editor)

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Hardcover | 5 December 2007

At a Glance

Hardcover
428 Pages

Dimensions(cm)
24.13 x 15.88 x 2.54

Hardcover

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Multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text aimed at researchers and practitioners in the area of search and optimization. The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers deal with the concepts of problem, solution, objective, constraint, utility and preference, and show how these concepts are being investigated in current practice. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multiobjective approach. Each chapter contributes to the central, deep concepts and themes of the book.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Mathematics Optimisation Linear Programming Engineering & Technology Technology in General Technical Design

Applied Mathematics

Introduction: Problem Solving, EC and EMO	p. 1
Exploiting Multiple Objectives: From Problems to Solutions
Multiobjective Optimization and Coevolution	p. 31
Constrained Optimization via Multiobjective Evolutionary Algorithms	p. 53
Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms	p. 77
Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms	p. 93
Can Single-Objective Optimization Profit from Multiobjective Optimization?	p. 115
Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making	p. 131
Machine Learning with Multiple Objectives
Multiobjective Supervised Learning	p. 155
Reducing Bloat in GP with Multiple Objectives	p. 177
Multiobjective GP for Human-Understandable Models: A Practical Application	p. 201
Multiobjective Classification Rule Mining	p. 219
Multiple Objectives in Design and Engineering
Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization	p. 243
User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria	p. 263
Multi-competence Cybernetics: The Study of Multiobjective Artificial Systems and Multi-fitness Natural Systems	p. 285
Scaling up Multiobjective Optimization
Fitness Assignment Methods for Many-Objective Problems	p. 307
Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms	p. 331
Objective Set Compression	p. 357
On Handling a Large Number of Objectives A Posteriori and During Optimization	p. 377
Index	p. 405
Table of Contents provided by Ingram. All Rights Reserved.

Multiobjective Problem Solving from Nature

From Concepts to Applications

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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