A genetically-optimised artificial life algorithm for complexity-based synthetic dataset generation

نویسندگان

چکیده

Algorithmic evaluation is a vital step in developing new approaches to machine learning and relies on the availability of existing datasets. However, real-world datasets often do not cover necessary complexity space required understand an algorithm’s domains competence. As such, generation synthetic fill gaps has gained attention, offering means evaluating algorithms when data unavailable. Existing complexity-focused are limited their ability generate solutions that invoke similar classification behaviour real data. The present work proposes novel method (Sy:Boid) for complexity-based generation, adapting extending Boid algorithm was originally intended computer graphics simulations. Sy:Boid embeds modified within evolutionary multi-objective optimisation which satisfy predefined magnitudes measures. evaluated compared labelling-based sampling-based its wide variety realistic Results demonstrate capable generating across greater portion than approaches. Furthermore, produced were observed very behaviours

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ژورنال

عنوان ژورنال: Information Sciences

سال: 2023

ISSN: ['0020-0255', '1872-6291']

DOI: https://doi.org/10.1016/j.ins.2022.11.015