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Books on the topic 'Local search heuristics'

Author: Grafiati
Published: 6 September 2023
Last updated: 25 May 2024

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1

Voß, Stefan, Silvano Martello, Ibrahim H. Osman, and Catherine Roucairol, eds. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5775-3.

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2

Stützle, Thomas, Mauro Birattari, and Holger H. Hoos, eds. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74446-7.

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3

Stützle, Thomas, Mauro Birattari, and Holger H. Hoos, eds. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03751-1.

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4

Vo€, Stefan. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Boston, MA: Springer US, 1999.

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5

Stefan, Voss, and Meta-Heuristics International Conference (2nd : 1997 : Sophia-Antipolis, France), eds. Meta-heuristics: Advances and trends in local search paradigms for optimization. Boston, Mass: Kluwer Academic Publishers, 1999.

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6

Thomas, Stützle, Birattari Mauro, and Hoos Holger H, eds. Engineering stochastic local search algorithms: Designing, implementing and analyzing effective heuristics : international workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007 : proceedings. Berlin: Springer, 2007.

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7

H, Hoos Holger, Birattari Mauro, and SpringerLink (Online service), eds. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: Second International Workshop, SLS 2009, Brussels, Belgium, September 3-4, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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8

L, Aarts E. H., and Lenstra J. K, eds. Local search in combinatorial optimization. Princeton: Princeton University Press, 2003.

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9

L, Aarts E. H., and Lenstra J. K, eds. Local search in combinatorial optimization. Chichester [England]: Wiley, 1997.

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10

(Editor), Stefan Voß, Silvano Martello (Editor), Ibrahim H. Osman (Editor), and Cathérine Roucairol (Editor), eds. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Springer, 1998.

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11

Hoos, Holger H., Thomas Stützle, and Mauro Birattari. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings. Springer London, Limited, 2007.

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12

Michiels, Wil, Emile Aarts, and Jan Korst. Theoretical Aspects of Local Search. Springer London, Limited, 2007.

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13

Michiels, Wil, Emile Aarts, and Jan Korst. Theoretical Aspects of Local Search. Springer Berlin / Heidelberg, 2010.

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14

(Editor), Emile Aarts, and Jan Karel Lenstra (Editor), eds. Local Search in Combinatorial Optimization. Princeton University Press, 2003.

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15

Lenstra, Jan Karel, and Emile Aarts. Local Search in Combinatorial Optimization. Princeton University Press, 2018.

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16

Aarts, Emile. Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series). Springer, 2007.

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17

Michel, Bierlaire. Optimization: Principles and Algorithms. EPFL Press, 2015. http://dx.doi.org/10.55430/6116v1mb.

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Abstract:
Every engineer and decision scientist must have a good mastery of optimization, an essential element in their toolkit. Thus, this articulate introductory textbook will certainly be welcomed by students and practicing professionals alike. Drawing from his vast teaching experience, the author skillfully leads the reader through a rich choice of topics in a coherent, fluid and tasteful blend of models and methods anchored on the underlying mathematical notions (only prerequisites: first year calculus and linear algebra). Topics range from the classics to some of the most recent developments in smooth unconstrained and constrained optimization, like descent methods, conjugate gradients, Newton and quasi-Newton methods, linear programming and the simplex method, trust region and interior point methods. Furthermore elements of discrete and combinatorial optimization like network optimization, integer programming and heuristic local search methods are also presented. This book presents optimization as a modeling tool that beyond supporting problem formulation plus design and implementation of efficient algorithms, also is a language suited for interdisciplinary human interaction. Readers further become aware that while the roots of mathematical optimization go back to the work of giants like Newton, Lagrange, Cauchy, Euler or Gauss, it did not become a discipline on its own until World War Two. Also that its present momentum really resulted from its symbiosis with modern computers, which made it possible to routinely solve problems with millions of variables and constraints. With his witty, entertaining, yet precise style, Michel Bierlaire captivates his readers and awakens their desire to try out the presented material in a creative mode. One of the outstanding assets of this book is the unified, clear and concise rendering of the various algorithms, which makes them easily readable and translatable into any high level programming language. ''This is an addictive book that I am very pleased to recommend.'' Prof. Thomas M. Liebling
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