Journal article 70 views
BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
IEEE Transactions on Evolutionary Computation, Pages: 1 - 1
Swansea University Author: Fabio Caraffini
Full text not available from this repository: check for access using links below.
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource-and behaviour-based benchmarks to...
|Published in:||IEEE Transactions on Evolutionary Computation|
Institute of Electrical and Electronics Engineers (IEEE)
Check full text
No Tags, Be the first to tag this record!
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource-and behaviour-based benchmarks to test the resource consumption and the behaviour of algorithms. In this article, we propose a novel behaviour-based benchmark toolbox: BIAS (Bias in Algorithms, Structural). This toolbox can detect structural bias per dimension and across dimension based on 39 statistical tests. Moreover, it predicts the type of structural bias using a Random Forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for structural bias in an early phase of development. Experiments with a large set of generated structural bias scenarios show that BIAS was successful in identifying bias. In addition we also provide the results of BIAS on 432 existing state-of-the-art optimisation algorithms showing that different kinds of structural bias are present in these algorithms, mostly towards the centre of the objective space or showing discretization behaviour. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.
Faculty of Science and Engineering