This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated.
Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants.
Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.
This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation.
Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours - in terms of performance, disruptiveness and population diversity.
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making.
This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values.
Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty.