Stochastic methods for global optimization and problem solving

Giovanni Stracquadanio, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish
Title of host publicationEncyclopedia of Bioinformatics and Computational Biology
Subtitle of host publicationABC of Bioinformatics
PublisherElsevier
Pages321-327
Number of pages7
Volume1-3
ISBN (Electronic)9780128114322
ISBN (Print)9780128114148
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • derivative-free optimization
  • genetic algorithms
  • global optimization
  • simulated annealing

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