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When short runs beat long runs

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Sean LukeAuthors Info & Claims

GECCO'01: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation

Pages 74 - 80

Published: 07 July 2001 Publication History

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Abstract

What will yield the best results: doing one run n generations long or doing m runs n/m generations long each? This paper presents a technique-independent analysis which answers this question, and has direct applicability to scheduling and restart theory in evolutionary computation and other stochastic methods. The paper then applies this technique to three problem domains in genetic programming. It discovers that in two of these domains there is a maximal number of generations beyond which it is irrational to plan a run; instead it makes more sense to do multiple shorter runs.

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Friedrich TKötzing TQuinzan FSutton ATakadama K(2018)Improving the run time of the (1 + 1) evolutionary algorithm with luby sequencesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205525(301-308)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205525
Burks APunch W(2017)An analysis of the genetic marker diversity algorithm for genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-016-9281-918:2(213-245)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10710-016-9281-9
Walker MEdwards HMessom CLipson H(2007)The reliability of confidence intervals for computational effort comparisonsProceedings of the 9th annual conference on Genetic and evolutionary computation10.1145/1276958.1277294(1716-1723)Online publication date: 7-Jul-2007
https://dl.acm.org/doi/10.1145/1276958.1277294
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When short runs beat long runs
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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GECCO'01: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation

July 2001

1461 pages

ISBN:1558607749

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Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

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Published: 07 July 2001

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Friedrich TKötzing TQuinzan FSutton ATakadama K(2018)Improving the run time of the (1 + 1) evolutionary algorithm with luby sequencesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205525(301-308)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205525
Burks APunch W(2017)An analysis of the genetic marker diversity algorithm for genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-016-9281-918:2(213-245)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10710-016-9281-9
Walker MEdwards HMessom CLipson H(2007)The reliability of confidence intervals for computational effort comparisonsProceedings of the 9th annual conference on Genetic and evolutionary computation10.1145/1276958.1277294(1716-1723)Online publication date: 7-Jul-2007
https://dl.acm.org/doi/10.1145/1276958.1277294
Loveard T(2003)Genetic programming with meta-searchProceedings of the 6th European conference on Genetic programming10.5555/1762668.1762680(119-129)Online publication date: 14-Apr-2003
https://dl.acm.org/doi/10.5555/1762668.1762680

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Accurately choosing execution runs for software fault localization

Short-Selling Attacks and Creditor Runs

Runs of geometrically distributed random variables: a probabilistic analysis

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