Stochastic methods for global optimization and problem solving

Giovanni Stracquadanio; Panos M. Pardalos

doi:10.1016/B978-0-12-809633-8.20329-4

Stochastic methods for global optimization and problem solving

Giovanni Stracquadanio, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Stochastic algorithms provide an effective framework to solve complex optimization problems, when derivative information is not available. Simulated Annealing and Genetic Algorithms are stochastic optimization methods that can identify putative optimal solutions without using derivative information. This article presents the mathematical foundations and the algorithmic framework of these two methods.

Original language	English
Title of host publication	Encyclopedia of Bioinformatics and Computational Biology
Subtitle of host publication	ABC of Bioinformatics
Publisher	Elsevier
Pages	321-327
Number of pages	7
Volume	1-3
ISBN (Electronic)	9780128114322
ISBN (Print)	9780128114148
DOIs	https://doi.org/10.1016/B978-0-12-809633-8.20329-4
Publication status	Published - 1 Jan 2018

Keywords

derivative-free optimization
genetic algorithms
global optimization
simulated annealing

Access to Document

10.1016/B978-0-12-809633-8.20329-4

Cite this

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title = "Stochastic methods for global optimization and problem solving",

abstract = "Stochastic algorithms provide an effective framework to solve complex optimization problems, when derivative information is not available. Simulated Annealing and Genetic Algorithms are stochastic optimization methods that can identify putative optimal solutions without using derivative information. This article presents the mathematical foundations and the algorithmic framework of these two methods.",

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AU - Pardalos, Panos M.

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AB - Stochastic algorithms provide an effective framework to solve complex optimization problems, when derivative information is not available. Simulated Annealing and Genetic Algorithms are stochastic optimization methods that can identify putative optimal solutions without using derivative information. This article presents the mathematical foundations and the algorithmic framework of these two methods.

KW - derivative-free optimization

KW - genetic algorithms

KW - global optimization

KW - simulated annealing

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DO - 10.1016/B978-0-12-809633-8.20329-4

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