Overview

The origins of evolutionary computation can be traced back to the late 1950s where it remained, almost unknown, to the broader scientific community for three decades until the 1980s when it started to receive significant attention, as did the study of multi-agent systems (MAS). This focuses on systems in which many intelligent agents interact with each other. Today these systems are not simply a research topic but are also beginning to become an important subject of academic teaching and industrial and commercial application. Co-evolutionary Algorithm & Multi-agent Systems introduces the author's recent work in these two new and important branches of artificial intelligence.

Full Product Details

Author:   Licheng Jiao ,  J. Liu ,  Wan-Xie Zhong (Dept. of Engineering Mechanics, Dalian University of Technology, PR of China)
Publisher:   WIT Press
Imprint:   WIT Press
ISBN:  

9781845646387

ISBN 10:   184564638
Pages:   270
Publication Date:   16 December 2011
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Table of Contents Chapter 1 Introduction Evolutionary computation; Structure of evolutionary algorithms; Branches of evolutionary algorithms; Evolutionary computation and complex adaptive systems; Multiagent systems; Agents; MAS Chapter 2 An organizational coevolutionary algorithm for classification Related work; Organizations for classification; Fitness of organizations; Implementation of OCEC; Evaluation of OCEC's effectiveness; Multiplexer problems; Experimental results; Comparison of OCEC with available algorithms; Comparison on UCI repository datasets; Comparison of OCEC with XCS on multiplexer problems; Scalability of OCEC; Practical applications of OCEC; Radar target recognition problems; Remote sensing target recognition;Conclusion Chapter 3 An organizational evolutionary algorithm for satisfiability problems Organizations for SAT problems; Organizational evolutionary operators; Implementation of OEA_SAT; Experiments; Conclusion Chapter 4 An organizational evolutionary algorithm for numerical optimization Organizations for numerical optimization; Evolutionary operators for organizations; Splitting operator; Annexing operator; Cooperating operator; Implementation of OEA; Convergence of OEA; Experiments on unconstrained optimization problems; Experimental results of OEA; Comparison between OEA and FEP; Comparison between OEA and OGA/Q; Experiments on constrained optimization problems; Experimental results of OEA; Comparison between OEA and RY; Comparison between OEA and SMES on G01 to G13; Comparison between OEA and SCA on the four engineering design problems; Parameter analyses of OEA; Effects of No on the performance of OEA; Effects of AS and CS on the Performance of OEA; Effects of MaxOS on the performance of OEA; Conclusion Chapter 5 Moving block sequence: a new VLSI floorplan representation 5.1 Related work 83 5.2 Moving block sequence representation 84 5.3 Algorithm transforming an MBS to a floorplan 87 5.3.1 Information structure for rectilinear blocks 87 5.3.2 Implementation of the algorithm 88 5.3.3 An example 93 5.3.4 Conclusion 94 Chapter 6 An organizational evolutionary algorithm for general floorplanning based on moving block sequence Related work; Organizations for floorplanning; Determining shapes and orientations of various types of blocks; Evolutionary operators for organizations; Implementation of the algorithm; Experiments; Floorplanning problems with hard rectangular blocks; Floorplanning problems with soft rectangular blocks; Floorplanning problems with hybrid blocks; Floorplanning problems with concave blocks; Finding the strength of MBS-OEA; The MBS; The OEA; Conclusion Chapter 7 A multiagent genetic algorithm for global numerical optimization Agents for numerical optimization; Four evolutionary operators for agents; Implementation of MAGA; Convergence of MAGA; Experiments; Descriptions of the compared algorithms; Comparison between FEP, OGA/Q, and MAGA on functions with 30 dimensions; The performance of MAGA on functions with 20 to 1,000 dimensions; Performance of MAGA on functions with 1,000 to 10,000 Dimensions; Experimental studies on the optimal approximation of linear systems; MAGA for the optimal approximation of linear systems; Experiments; Conclusion Chapter 8 A Macroagent evolutionary model for decomposable function optimization Definition of decomposable functions; Macroagent; Macroagent evolutionary model; Hierarchy multiagent Genetic Algorithm; Experiments on HMAGA; Comparison between HMAGA and MAGA on Rosenbrock function with 10 to 1,000 dimensions; Performance of HMAGA on the Rosenbrock function with 1,000 to 50,000 dimensions; Performance analysis of parameter kappa; Conclusion Chapter 9 A multiagent evolutionary algorithm for combinatorial optimization problems Agents for combinatorial optimization problems; Behaviors of agents; Competition behaviour; Self-learning behaviour; Multiagent evolutionary algorithm for combinatorial optimization problems; Implementation of MAEA-CmOPs; Convergence of MAEA-CmOPs; Experiments on deceptive problems; Strong-linkage deceptive functions; Weak-linkage deceptive functions; Overlapping-linkage deceptive functions; Experiments on hierarchical problems; Hierarchical problems; Experiments and analyses; Conclusion Chapter 10 A multiagent evolutionary algorithm for constraint satisfaction problem Constraint satisfaction agents; Constraint satisfaction problems; Definition of constraint satisfaction agents; Environment of constraint satisfaction agents; Behaviors of constraint satisfaction agents; Competitive behaviour; Self-learning behaviour; Mutation behaviour; Implementation of MAEA-CSPs; Complexity analysis; Space complexity of MAEA-CSPs; Convergence of MAEA-CSPs; Experimental studies on non-permutation constraint satisfaction problems; Binary constraint satisfaction problems; Graph-coloring problems; Experimental studies on permutation constraint satisfaction problems; n-Queen problems; Job-shop scheduling problems; Conclusion

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Author Information

Licheng JIAO (Ph.D. Xi'an Jiaotong University, 1990) has been a full professor in the School of Electronic Engineering at Xidian University since 1992, having been a postdoctoral fellow in the National Key Laboratory for Radar Signal Processing, Xidian University, Xi'an, China, from 1990 to 1991. Currently, he is the Dean of the Electronic Engineering School and the Director of the Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China at Xidian University. In 1992, Dr. Jiao was awarded the Youth Science and Technology Award. In 1996, he was granted the Cross-Century Specialists Fund from the Ministry of Education of China, and also selected as a member of the First Level of Millions of Talents Project of China. In 2006, he was selected as an Expert with Special Contribution to Shaanxi Province. In 2007, as a principal member, he and his colleagues founded an Innovative Research Team of the Ministry of Education of China. Dr. Jiao is a Senior Member of IEEE, a member of IEEE Xi'an Section Execution Committee, the Chairman of Awards and Recognition Committee, vice board chairperson of Chinese Association of Artificial Intelligence, councilor of Chinese Institute of Electronics, committee member of Chinese Committee of Neural Networks, and expert of Academic Degrees Committee of the State Council. His research interests include image processing, natural computation, machine learning, and intelligent information processing. He has been the principle investigator of over 40 important scientific research projects, published more than 20 monographs, and a hundred of papers in international journals and conferences. Jing LIU (Ph.D., Institute of Intelligent Information Processing, Xidian University, 2004, circuits and systems) joined Xidian University in 2005 as a lecturer, and was promoted to a full professor in 2009. From 2007 to 2008, she worked in The University of Queensland, Australia as a postdoctoral research fellow, and from 2009 to 2011, she worked in University of New South Wales as a research associate with the Australian Defence Force Academy. Her research interests include evolutionary computation, complex networks, multiagent systems, and data mining. She has co-authored 2 monographs and more than 40 research papers which were published by international journals and conferences. Weicai ZHONG (Ph.D., Institute of Intelligent Information Processing, Xidian University, 2004, pattern recognition and intelligent information systems) is also with Xidian University. He worked at SPSS Inc. (Xi'an, China) as a Senior Statistician from 2005 to 2007. In 2008, he worked at the University of Ottawa, Canada, as a postdoctoral research fellow, and from 2009 to 2010, he worked at the University of New South Wales as a research associate with the Australian Defence Force Academy. His research interests include evolutionary game, complex networks, evolutionary computation, and statistical learning. He has co-authored one monograph and more than 30 research papers published by international journals and conferences.

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