Evolutionary algorithms under noise and uncertainty: A location-allocation case study

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Evolutionary approaches are metaheuristics that can deal with the effect of noise and uncertainty in data using different strategies. In this paper is depicted the method used to cope with these elements in a dynamical location-allocation problem. The use of Monte Carlo sampling and statistical historical data that can be applied to a single and multi-objective problems and within an online and offline scenario is tested and evaluated.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherIEEE
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 13 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

Fingerprint

Location-allocation
Evolutionary algorithms
Evolutionary Algorithms
Sampling
Uncertainty
Monte Carlo Sampling
Historical Data
Metaheuristics
Scenarios
Allocation problem
Evolutionary
Strategy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization
  • Artificial Intelligence

Cite this

Vallejo, Marta ; Corne, David W. / Evolutionary algorithms under noise and uncertainty : A location-allocation case study. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017.
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Vallejo, M & Corne, DW 2017, Evolutionary algorithms under noise and uncertainty: A location-allocation case study. in 2016 IEEE Symposium Series on Computational Intelligence (SSCI)., 7849959, IEEE, 2016 IEEE Symposium Series on Computational Intelligence, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7849959

Evolutionary algorithms under noise and uncertainty : A location-allocation case study. / Vallejo, Marta; Corne, David W.

2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. 7849959.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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