Article

Parallel genetic algorithm: assessment of performance in multidimensional scaling

Authors:

Antanas Zilinskas,

Julius ZilinskasAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 1492 - 1501

https://doi.org/10.1145/1276958.1277229

Published: 07 July 2007 Publication History

Abstract

Visualization of multidimensional data by means of Multidimensional Scaling (MDS) is a popular technique of exploratory data analysis widely usable, e.g. in analysis of bio-medical data, behavioral science, marketing research, etc. Implementations of MDS methods include a subroutine for an auxiliary global optimization problem. The latter is difficult because of high dimensionality, absence of overall smoothness, and a large number of local minima. In such a situation application of a genetic algorithm (GA) seems reasonable. A favorable assessment of application of GAs in MDS in previous publications is based on heuristic arguments without estimating quantitatively the precision of GA while applied to the solution of corresponding global optimization problems. Indeed, the estimation of precision is difficult because of complexity to find the actual global minimum not only in routine use but also in unique research experiments. Quantitatively the precision of GA was estimated, at least in the experimental problems of modest dimensionality, using global minima found by means of the developed parallel version of explicit enumeration algorithm. To cope with high complexity of the minimization problem a parallel version of GA is developed, and its efficiency for problem of higher dimensionality is investigated.

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Cited By

Jakaitiene ASangiovanni MGuarracino MPardalos P(2016)Multidimensional Scaling for Genomic DataAdvances in Stochastic and Deterministic Global Optimization10.1007/978-3-319-29975-4_7(129-139)Online publication date: 5-Nov-2016
https://doi.org/10.1007/978-3-319-29975-4_7
Dzemyda GKurasova OŽilinskas JDzemyda GKurasova OŽilinskas J(2012)Optimization-Based VisualizationMultidimensional Data Visualization10.1007/978-1-4419-0236-8_3(41-112)Online publication date: 9-Sep-2012
https://doi.org/10.1007/978-1-4419-0236-8_3

Index Terms

Parallel genetic algorithm: assessment of performance in multidimensional scaling
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Quadratic programming
      2. Mixed discrete-continuous optimization
        Integer programming
  2. Mathematical software
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Quadratic programming
      2. Mixed discrete-continuous optimization
        Integer programming

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Jakaitiene ASangiovanni MGuarracino MPardalos P(2016)Multidimensional Scaling for Genomic DataAdvances in Stochastic and Deterministic Global Optimization10.1007/978-3-319-29975-4_7(129-139)Online publication date: 5-Nov-2016
https://doi.org/10.1007/978-3-319-29975-4_7
Dzemyda GKurasova OŽilinskas JDzemyda GKurasova OŽilinskas J(2012)Optimization-Based VisualizationMultidimensional Data Visualization10.1007/978-1-4419-0236-8_3(41-112)Online publication date: 9-Sep-2012
https://doi.org/10.1007/978-1-4419-0236-8_3

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