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

Marta Vallejo; David W. Corne

doi:10.1109/SSCI.2016.7849959

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

Marta Vallejo, David W. Corne

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

10 Citations (Scopus)

194 Downloads (Pure)

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 language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Publisher	IEEE
ISBN (Electronic)	9781509042401
DOIs	https://doi.org/10.1109/SSCI.2016.7849959
Publication status	Published - 13 Feb 2017
Event	2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence
Abbreviated title	SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16

ASJC Scopus subject areas

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

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10.1109/SSCI.2016.7849959

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title = "Evolutionary algorithms under noise and uncertainty: A location-allocation case study",

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.",

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AB - 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.

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

Abstract

Conference

ASJC Scopus subject areas

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