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Math oracles: a new way of designing efficient self-adaptive algorithms

Published: 06 July 2013 Publication History

Abstract

In this paper we present a new general methodology to develop self-adaptive methods at a low computational cost. Instead of going purely ad-hoc we define several simple steps to include theoretical models as additional information in our algorithm. Our idea is to incorporate the predictive information (future behavior) provided by well-known mathematical models or other prediction systems (the oracle) to build enhanced methods. We show the main steps which should be considered to include this new kind of information into any algorithm. In addition, we actually test the idea on a specific algorithm, a genetic algorithm (GA). Experiments show that our proposal is able to obtain similar, or even better results when it is compared to the traditional algorithm. We also show the benefits in terms of saving time and a lower complexity of parameter settings.

Reference

[1]
G. Luque and E. Alba. Analyzing the behaviour of population-based algorithms using Rayleigh distribution. In PPSN XII, pages 417--427, 2012.

Cited By

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  • (2021)Metaheuristics and Software Engineering: Past, Present, and FutureInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402150044331:09(1349-1375)Online publication date: 3-Oct-2021
  • (2018)How Can Metaheuristics Help Software Engineers?Search-Based Software Engineering10.1007/978-3-319-99241-9_4(89-105)Online publication date: 22-Aug-2018

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Published In

cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. mathematical oracles
  2. methodology
  3. self-adaptive techniques

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Conference

GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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

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Cited By

View all
  • (2021)Metaheuristics and Software Engineering: Past, Present, and FutureInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402150044331:09(1349-1375)Online publication date: 3-Oct-2021
  • (2018)How Can Metaheuristics Help Software Engineers?Search-Based Software Engineering10.1007/978-3-319-99241-9_4(89-105)Online publication date: 22-Aug-2018

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