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Local Search in Combinatorial Optimization

  • 536 Pages
  • 0.51 MB
  • 2809 Downloads
  • English

Princeton University Press
Combinatorics & graph theory, Optimization, Heuristic programming, Arithmetic, Mathematics, Science/Mathematics, Linear Programming, Applied, General, Mathematics / Advanced, Algorithms, Combinatorial optimiz
ContributionsEmile Aarts (Editor), Jan Karel Lenstra (Editor)
The Physical Object
FormatPaperback
ID Numbers
Open LibraryOL7758685M
ISBN 100691115222
ISBN 139780691115221

Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority. This book is an important reference and invaluable source of inspiration for students and researchers Local Search in Combinatorial Optimization book discrete mathematics, computer science, operations research, industrial engineering, and management science.

Download Local Search in Combinatorial Optimization FB2

Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority.

This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science.3/5(1).

In the past three decades local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization. Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in a reasonable time.

Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority. This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science.5/5(1).

TY - BOOK. T1 - Local search in combinatorial optimization. A2 - Aarts, E.H.L. A2 - Lenstra, J.K. PY - Y1 - M3 - Book editing.

Details Local Search in Combinatorial Optimization FB2

SN - Cited by: PDF | On Jan 1,James B. Orlin and others published Local Search in Combinatorial Optimization | Find, read and cite all the research you need on ResearchGate.

Genetic Algorithm Local Search Tabu Search Travel Salesman Problem Crossover Operator These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm by: In operations research, applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects.

In many such problems, exhaustive search is not tractable. It operates on the domain of those optimization problems in which the set of feasible solutions is discrete or can be reduced to discrete, and in.

Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority.

This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research 3/5(4). Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority.

This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science.3/5(4).

Combinatorial optimization is one of the youngest and most active areas of discrete mathematics, and is probably its driving force today. This book describes the most important ideas, theoretical results, and algorithms of this field. Test Construction as a Combinatorial Optimization Problem.

Combinatorial optimization problems are those where mathematical techniques are applied to find optimal solutions within a finite set of possible solutions.

The set of possible solutions is generally defined by a set of restrictions, and the set is too large for exhaustive search. Local Search in Combinatorial Optimization | Emile Aarts, Jan Karel Lenstra | download | B–OK.

Download books for free. Find books. Buy Local Search in Combinatorial Optimization With a New preface by the editors by Aarts, Emile, Lenstra, Jan Karel (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders. On one hand this book serves as a good introduction to combinatorial optimization algorithms, in that it provides a flawless introduction to the simplex algorithm, linear and integer programming, and search techniques such as Branch-and-Bound and dynamic by: The problem Local Search, which finds a local minimum of a black-box function on a given graph, is of both practical and theoretical importance to combinatorial optimization, complexity theory.

- Buy Combinatorial Optimization: Algorithms and Complexity (Dover Books on Computer Science) book online at best prices in India on Read Combinatorial Optimization: Algorithms and Complexity (Dover Books on Computer Science) book reviews & author details and more at Free delivery on qualified orders/5(29).

Local search in combinatorial optimization. [E H L Aarts; J K Lenstra;] Covers local search and its variants from both a theoretical and practical point of view. This book is suitable for students and researchers in discrete mathematics.

The paper presents a new genetic local search (GLS) algorithm for multi-objective combinatorial optimization (MOCO). The goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise by: "Local Search in Combinatorial Optimization" covers Local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority.

This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research. This repo provides the code to replicate the experiments in the paper Xinyun Chen, Yuandong Tian, Learning to Perform Local Rewriting for Combinatorial Optimization, in NeurIPS For expression simplification, given an initial expression (in Halide for our evaluation), the goal is to find an.

Local search approaches to combinatorial optimization are able to isolate optimal or near-optimal solutions within reasonable time constraints. This book introduces a method for solving combinatorial optimization problems that combines constraint programming and local search, using constraints to describe and control local search, and a.

This book constitutes the refereed proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOPheld in Málaga, Spain, in Aprilcolocated with the Evo* events EuroGP, EvoBIO, EvoMUSART, and EvoApplications.

Brand: Springer-Verlag Berlin Heidelberg. The 14 full papers presented in this book were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to combinatorial optimization problems.

Emile Aarts and Jan Karel Lenstra, "Local Search in Combinatorial Optimization", Princeton University Press, 14 July, ISBN: Other subject areas related to Local Search in Combinatorial Optimization (possibly beyond the scope of this System Dynamics Glossary) include: Algorithms, Applied, Arithmetic, Combinatorial optimization, General, Heuristic programming, Linear.

On one hand this book serves as a good introduction to combinatorial optimization algorithms, in that it provides a flawless introduction to the simplex algorithm, linear and integer programming, and search techniques such as Branch-and-Bound and dynamic programming/5(29). This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOPheld in Torino, Italy, in April The 22 revised full papers presented were carefully reviewed and selected from 42 submissions.

The papers. Summary: In the past three decades local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization.

Description Local Search in Combinatorial Optimization FB2

Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in a reasonable time.

The Traveling Salesman Problem: A Case Study in Local Optimization David S. Johnson1 Lyle A. McGeoch2 Abstract This is a preliminary version of a chapter that appeared in the bookLocal Search in Combinatorial Optimization, E.

Aarts and J. Lenstra (eds.), John Wiley and Sons, London,pp. Local Search in Combinatorial Optimization Book Description: In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention.

Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority.

This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management : $  Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management.

The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area.The use of local search in combinatorial optimization has a long history that reaches back to the late s and early s, when the first edge-exchange algorithms for the traveling salesman problem (TSP) were introduced: see the work of Hock [a,b], Croes [], Lin [], and Reiter & .