BBIOS: A Characterization of Evolutionary Algorithm Stability

MJ Craven; SP Martin

doi:10.1145/3067695.3082034

BBIOS: A Characterization of Evolutionary Algorithm Stability

MJ Craven
, SP Martin

School of Engineering, Computing and Mathematics

Research output: Contribution to journal › Conference proceedings published in a journal › peer-review

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Abstract

We report preliminary results of linking distinct parameter metrics and stability via a novel dynamical system stability characterization (BBIOS). We conduct EA trials to determine the extent of EA stability in parameter space neighborhoods defined by metrics. We capture EA performance loss due to perturbation.

Original language	English
Number of pages	0
Journal	GECCO 2017 Companion
Volume	0
Issue number	0
DOIs	https://doi.org/10.1145/3067695.3082034
Publication status	Published - 18 Jul 2017
Event	GECCO 2017 - Berlin, Germany Duration: 15 Jul 2017 → 19 Jul 2017

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10.1145/3067695.3082034

Bbios-characterization-evolutionary

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abstract = "We report preliminary results of linking distinct parameter metrics and stability via a novel dynamical system stability characterization (BBIOS). We conduct EA trials to determine the extent of EA stability in parameter space neighborhoods defined by metrics. We capture EA performance loss due to perturbation.",

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AB - We report preliminary results of linking distinct parameter metrics and stability via a novel dynamical system stability characterization (BBIOS). We conduct EA trials to determine the extent of EA stability in parameter space neighborhoods defined by metrics. We capture EA performance loss due to perturbation.

UR - https://pearl.plymouth.ac.uk/secam-research/862/

U2 - 10.1145/3067695.3082034

DO - 10.1145/3067695.3082034

M3 - Conference proceedings published in a journal

VL - 0

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JF - GECCO 2017 Companion

IS - 0

T2 - GECCO 2017

Y2 - 15 July 2017 through 19 July 2017

ER -

BBIOS: A Characterization of Evolutionary Algorithm Stability

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