Electronic Resource

Article - Deep-BIAS: Detecting Structural Bias using Explainable AI Vol: - (Issue): - Hal: 455–458

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Evaluating the performance of heuristic optimisation algorithms is essential to determine how well
they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour
benchmark to detect structural bias (SB) in search algorithms. The toolbox can be used to identify
biases in existing algorithms, as well as to test for bias in newly developed algorithms. In this article,

we introduce a novel and explainable deep-learning expansion of the BIAS toolbox, called Deep-
BIAS. Where the original toolbox uses 39 statistical tests and a Random Forest model to predict the

existence and type of SB, the Deep-BIAS method uses a trained deep-learning model to immediately
detect the strength and type of SB based on the raw performance distributions. Through a series
of experiments with a variety of structurally biased scenarios, we demonstrate the effectiveness of
Deep-BIAS. We also present the results of using the toolbox on 336 state-of-the-art optimisation
algorithms, which showed the presence of various types of structural bias, particularly towards
the centre of the objective space or exhibiting discretisation behaviour. The Deep-BIAS method
outperforms the BIAS toolbox both in detecting bias and for classifying the type of SB. Furthermore,
explanations can be derived using XAI techniques.

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Ketersediaan

Informasi Detail

Judul Seri

-

No. Panggil

006.3 STE d

Penerbit

: Association for Computing Machinery (ACM), New York, NY, USA., 2023

Deskripsi Fisik

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Bahasa

English

ISBN/ISSN

979-8-4007-0120-7

Klasifikasi

006.3 STE d

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

Vol: - (Issue): - Hal: 455–458

Subjek

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Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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