TY  - BOOK
AU  - Stützle,Thomas
AU  - Birattari,Mauro
AU  - Hoos,Holger H.
ED  - SpringerLink (Online service)
TI  - Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: Second International Workshop, SLS 2009, Brussels, Belgium, September 3-4, 2009. Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9783642037511
AV  - QA76.9.D35 
U1  - 005.73 23
PY  - 2009///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Data structures (Computer science)
KW  - Computer software
KW  - Logic design
KW  - Data mining
KW  - Computer Science
KW  - Data Structures
KW  - Data Structures, Cryptology and Information Theory
KW  - Data Storage Representation
KW  - Algorithm Analysis and Problem Complexity
KW  - Logics and Meanings of Programs
KW  - Data Mining and Knowledge Discovery
N1  - High-Performance Local Search for Task Scheduling with Human Resource Allocation -- High-Performance Local Search for Task Scheduling with Human Resource Allocation -- On the Use of Run Time Distributions to Evaluate and Compare Stochastic Local Search Algorithms -- Estimating Bounds on Expected Plateau Size in MAXSAT Problems -- A Theoretical Analysis of the k-Satisfiability Search Space -- Loopy Substructural Local Search for the Bayesian Optimization Algorithm -- Running Time Analysis of ACO Systems for Shortest Path Problems -- Techniques and Tools for Local Search Landscape Visualization and Analysis -- Short Papers -- High-Performance Local Search for Solving Real-Life Inventory Routing Problems -- A Detailed Analysis of Two Metaheuristics for the Team Orienteering Problem -- On the Explorative Behavior of MAX–MIN Ant System -- A Study on Dominance-Based Local Search Approaches for Multiobjective Combinatorial Optimization -- A Memetic Algorithm for the Multidimensional Assignment Problem -- Autonomous Control Approach for Local Search -- EasyGenetic: A Template Metaprogramming Framework for Genetic Master-Slave Algorithms -- Adaptive Operator Selection for Iterated Local Search -- Improved Robustness through Population Variance in Ant Colony Optimization -- Mixed-Effects Modeling of Optimisation Algorithm Performance
N2  - Stochastic local search (SLS) algorithms are established tools for the solution of computationally hard problems arising in computer science, business adm- istration, engineering, biology, and various other disciplines. To a large extent, their success is due to their conceptual simplicity, broad applicability and high performance for many important problems studied in academia and enco- tered in real-world applications. SLS methods include a wide spectrum of te- niques, ranging from constructive search procedures and iterative improvement algorithms to more complex SLS methods, 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. In recent years, it has become - creasingly evident that success with SLS algorithms depends not merely on the adoption and e?cient implementation of the most appropriate SLS technique for a given problem, but also on the mastery of a more complex algorithm - gineering process. Challenges in SLS algorithm development arise partly from the complexity of the problems being tackled and in part from the many - grees of freedom researchers and practitioners encounter when developing SLS algorithms. Crucial aspects in the SLS algorithm development comprise al- rithm design, empirical analysis techniques, problem-speci?c background, and background knowledge in several key disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics
UR  - http://dx.doi.org/10.1007/978-3-642-03751-1
ER  -