Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics : International Workshop, SLS 2009, Brussels, Belgium, September 3-5, 2009, Proceedings / edited by Thomas Stützle, Mauro Birattari, Holger H. Hoos.

Format:

Book

Contributor:

Stützle, Thomas, editor.

Birattari, Mauro, editor.

Hoos, Holger H., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 5752.

Theoretical Computer Science and General Issues ; 5752

Language:

English

Subjects (All):

Computer programming.

Data structures (Computer science).

Algorithms.

Computer logic.

Programming Techniques.

Data Structures.

Data Structures and Information Theory.

Data Storage Representation.

Algorithm Analysis and Problem Complexity.

Logics and Meanings of Programs.

Local Subjects:

Programming Techniques.

Data Structures.

Data Structures and Information Theory.

Data Storage Representation.

Algorithm Analysis and Problem Complexity.

Logics and Meanings of Programs.

Physical Description:

1 online resource (X, 155 pages).

Edition:

First edition 2009.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-642-03751-1

9783642037511

Access Restriction:

Restricted for use by site license.