LEADER 05504nam 2200733Ia 450 001 9910824287603321 005 20240404142605.0 010 $a1-281-93474-7 010 $a9786611934743 010 $a981-279-485-9 035 $a(CKB)1000000000537798 035 $a(EBL)1679472 035 $a(OCoLC)879023572 035 $a(SSID)ssj0000209451 035 $a(PQKBManifestationID)11196632 035 $a(PQKBTitleCode)TC0000209451 035 $a(PQKBWorkID)10265627 035 $a(PQKB)11772155 035 $a(MiAaPQ)EBC1679472 035 $a(WSP)00005470 035 $a(Au-PeEL)EBL1679472 035 $a(CaPaEBR)ebr10255771 035 $a(CaONFJC)MIL193474 035 $a(EXLCZ)991000000000537798 100 $a20040611d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNetwork-based distributed planning using coevolutionary algorithms /$fRaj Subbu, Arthur C Sanderson 205 $a1st ed. 210 $aRiver Edge, NJ $cWorld Scientific$d2004 215 $a1 online resource (193 p.) 225 1 $aSeries in intelligent control and intelligent automation ;$vv. 13 300 $aDescription based upon print version of record. 311 $a981-238-754-4 320 $aIncludes bibliographical references (p. 159-168) and index. 327 $aContents ; Foreword ; Preface ; 1. Introduction ; 1.1 Motivation ; 1.2 Approach ; 1.3 Principal Contributions ; 1.4 Book Outline ; 2. Background and Related Work ; 2.1 Collaborative Manufacturing ; 2.1.1 Concurrent Engineering ; 2.1.2 Agile Manufacturing 327 $a2.2 Combinatorial Optimization 2.2.1 Deterministic Algorithms ; 2.2.2 Stochastic Algorithms ; 2.3 Evolutionary Algorithms ; 2.3.1 Principal Techniques ; 2.3.2 Theory and Applications ; 2.3.3 Techniques for Constrained Optimization ; 2.3.4 Multi-Node Algorithms 327 $a2.3.5 Techniques for Dynamic Environments 2.4 Agents ; 2.5 Distributed Problem Solving ; 3. Problem Formulation and Analysis ; 3.1 Introduction ; 3.2 General Problem Formulation ; 3.2.1 Constraints ; 3.2.2 Objectives ; 3.2.3 Optimization Problem ; 3.2.4 Complexity Analysis 327 $a3.3 Printed Circuit Assembly Problem 3.3.1 Complexity Analysis ; 3.4 Algorithm Applicability Analysis ; 3.4.1 Rationale ; 3.4.2 Problem Structure ; 3.4.3 Evaluation of Alternative Algorithms ; 3.4.4 Discussion ; 4. Theory and Analysis of Evolutionary Optimization ; 4.1 Introduction 327 $a4.2 Theoretical Foundation 4.2.1 Notation ; 4.2.2 General Algorithm ; 4.2.3 Basic Results ; 4.3 Convergence Analysis ; 4.3.1 Convergence for a Unimodal Objective ; 4.3.2 Convergence for a Bimodal Objective ; 5. Theory and Analysis of Distributed Coevolutionary Optimization 327 $a5.1 Introduction 330 $a In this book, efficient and scalable coevolutionary algorithms for distributed, network-based decision-making, which utilize objective functions are developed in a networked environment where internode communications are a primary factor in system performance. A theoretical foundation for this class of coevolutionary algorithms is introduced using techniques from stochastic process theory and mathematical analysis. A case study in distributed, network-based decision-making presents an implementation and detailed evaluation of the coevolutionary decision-making framework that incorporates di 410 0$aSeries in intelligent control and intelligent automation ;$vv. 13. 606 $aElectronic data processing$xDistributed processing 606 $aAlgorithms 606 $aIntelligent agents (Computer software) 615 0$aElectronic data processing$xDistributed processing. 615 0$aAlgorithms. 615 0$aIntelligent agents (Computer software) 676 $a004 676 $a005.2/76 676 $a005.276 700 $aSubbu$b Raj$01604839 701 $aSanderson$b A. C$g(Arthur C.)$028247 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824287603321 996 $aNetwork-based distributed planning using coevolutionary algorithms$93929825 997 $aUNINA