Experimental algorithmics, as its name indicates, combines algorithmic work and experimentation: algorithms are not just designed, but also implemented and tested on a variety of instances. Perhaps the most important lesson in this process is that designing an algorithm is but the first step in the process of developing robust and efficient software for applications. Based on a seminar held at Dagstuhl Castle, Germany in September 2000, this state-of-the-art survey presents a coherent survey of the work done in the area so far. The 11 carefully reviewed chapters provide complete coverage of all current topics in experimental algorithmics.

This is a guidebook for those who want to use computational experiments to support their work in algorithm design and analysis. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms.

Annotation. This volume constitutes the refereed proceedings of the 9th International Symposium on Experimental Algorithms, SEA 2010, held on Ischia Island, Naples, Italy, in May 2010. The 40 revised full papers presented together with two invited papers were carefully reviewed and selected from 73 submissions. The topics covered include algorithm engineering, algorithmic libraries, algorithmic mechanism design, analysis of algorithms, algorithms for memory hierarchies, approximation techniques, bioinformatics, branch and bound algorithms, combinatorial and irregular problems, combinatorial structures and graphs, communication networks, complex networks, computational geometry, computational learning theory, computational optimization, computer systems, cryptography and security, data streams, data structures, distributed and parallel algorithms, evaluation of algorithms for realistic environments, experimental techniques and statistics, graph drawing, heuristics for combinatorial optimization.

This book constitutes the refereed proceedings of the 8th International Symposium on Experimental and Efficient Algorithms, SEA 2009, held in Dortmund, Germany, in June 2009. The 23 revised full papers were carefully reviewed and selected from 64 submissions and present current research on experimental evaluation and engineering of algorithms, as well as in various aspects of computational optimization and its applications. Contributions are supported by experimental evaluation, methodological issues in the design and interpretation of experiments, the use of (meta- ) heuristics, or application-driven case studies that deepen the understanding of a problem's complexity.

This book constitutes the refereed proceedings of the 6th International Workshop on Experimental and Efficient Algorithms, WEA 2007, held in Rome, Italy, in June 2007. The 30 revised full papers presented together with three invited talks cover the design, analysis, implementation, experimental evaluation, and engineering of efficient algorithms.

This book constitutes the refereed proceedings of the Third International Workshop on Experimental and Efficient Algorithms, WEA 2004, held in Angra dos Reis, Brazil in May 2004. The 40 revised full papers presented together with abstracts of two invited talks were carefully reviewed and selected from numerous submissions. The book is devoted to the areas of design, analysis, and experimental evaluation of algorithms. Among the topics covered are scheduling, heuristics, combinatorial optimization, evolutionary optimization, graph computations, labeling, robot navigation, shortest path algorithms, flow problems, searching, randomization and derandomization, string matching, graph coloring, networking, error detecting codes, timetabling, sorting, energy minimization, etc.

One of Springer’s renowned Major Reference Works, this awesome achievement provides a comprehensive set of solutions to important algorithmic problems for students and researchers interested in quickly locating useful information. This first edition of the reference focuses on high-impact solutions from the most recent decade, while later editions will widen the scope of the work. All entries have been written by experts, while links to Internet sites that outline their research work are provided. The entries have all been peer-reviewed. This defining reference is published both in print and on line.

poggi@inf. puc-rio. br,rwerneck@cs. princeton. edu Abstract. Someofthemostwidelyusedconstructiveheuristicsforthe Steiner Problem in Graphs are based on algorithms for the Minimum Spanning Tree problem. In this paper, we examine e?cient implem- tations of heuristics based on the classic algorithms by Prim, Kruskal, and Bor? uvka.

To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments without demanding expert knowledge on simulation. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and describes its integration into the open-source modelling and simulation framework James II. Its selection mechanisms are able to cope with three situations: no prior knowledge is available, the impact of problem features on simulator performance is unknown, and a relationship between problem features and algorithm performance can be established empirically. The author concludes with an experimental evaluation of the developed methods.

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.