LEADER 06426nam 22008775 450 001 9910484565603321 005 20230302072028.0 010 $a3-540-74446-0 024 7 $a10.1007/978-3-540-74446-7 035 $a(CKB)1000000000490548 035 $a(SSID)ssj0000317492 035 $a(PQKBManifestationID)11240609 035 $a(PQKBTitleCode)TC0000317492 035 $a(PQKBWorkID)10289080 035 $a(PQKB)10821978 035 $a(DE-He213)978-3-540-74446-7 035 $a(MiAaPQ)EBC3061711 035 $a(MiAaPQ)EBC6386379 035 $a(PPN)123164478 035 $a(EXLCZ)991000000000490548 100 $a20100301d2007 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aEngineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics $eInternational Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings /$fedited by Thomas Stützle, Mauro Birattari, Holger H. Hoos 205 $a1st ed. 2007. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2007. 215 $a1 online resource (X, 230 p.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v4638 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-74445-2 320 $aIncludes bibliographical references and index. 327 $aThe Importance of Being Careful -- The Importance of Being Careful -- Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through -- Implementation Effort and Performance -- Tuning the Performance of the MMAS Heuristic -- Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions -- EasyAnalyzer: An Object-Oriented Framework for the Experimental Analysis of Stochastic Local Search Algorithms -- Mixed Models for the Analysis of Local Search Components -- An Algorithm Portfolio for the Sub-graph Isomorphism Problem -- A Path Relinking Approach for the Multi-Resource Generalized Quadratic Assignment Problem -- A Practical Solution Using Simulated Annealing for General Routing Problems with Nodes, Edges, and Arcs -- Probabilistic Beam Search for the Longest Common Subsequence Problem -- A Bidirectional Greedy Heuristic for the Subspace Selection Problem -- Short Papers -- EasySyn++: A Tool for Automatic Synthesis of Stochastic Local Search Algorithms -- Human-Guided Enhancement of a Stochastic Local Search: Visualization and Adjustment of 3D Pheromone -- Solving a Bi-objective Vehicle Routing Problem by Pareto-Ant Colony Optimization -- A Set Covering Approach for the Pickup and Delivery Problem with General Constraints on Each Route -- A Study of Neighborhood Structures for the Multiple Depot Vehicle Scheduling Problem -- Local Search in Complex Scheduling Problems -- A Multi-sphere Scheme for 2D and 3D Packing Problems -- Formulation Space Search for Circle Packing Problems -- Simple Metaheuristics Using the Simplex Algorithm for Non-linear Programming. 330 $aStochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. To a large degree, this popularity is based on the conceptual simplicity of many SLS methods and on their excellent performance on a wide gamut of problems, ranging from rather abstract problems of high academic interest to the very s- ci?c problems encountered in many real-world applications. SLS methods range from quite simple construction procedures and iterative improvement algorithms to more complex general-purpose schemes, also widely known as metaheuristics, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition, and overall resembled more an art than a science. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-speci?c background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v4638 606 $aArtificial intelligence?Data processing 606 $aInformation retrieval 606 $aComputer architecture 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aMathematical statistics 606 $aData mining 606 $aInformation storage and retrieval systems 606 $aData Science 606 $aData Storage Representation 606 $aAlgorithms 606 $aProbability and Statistics in Computer Science 606 $aData Mining and Knowledge Discovery 606 $aInformation Storage and Retrieval 615 0$aArtificial intelligence?Data processing. 615 0$aInformation retrieval. 615 0$aComputer architecture. 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 0$aMathematical statistics. 615 0$aData mining. 615 0$aInformation storage and retrieval systems. 615 14$aData Science. 615 24$aData Storage Representation. 615 24$aAlgorithms. 615 24$aProbability and Statistics in Computer Science. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 676 $a518.1 702 $aStützle$b Thomas 702 $aBirattari$b Mauro 702 $aHoos$b Holger H. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910484565603321 996 $aEngineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics$9772147 997 $aUNINA