All Outputs (3)

Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics. (2024)
Thesis
FYVIE, M. 2024. Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2565263

Evolutionary algorithms (EAs) are the principal focus of research study in Evolutionary Computing (EC). In EC, naturally occurring processes designed to drive success in nature are simulated for a similar purpose in numerical optimisation. Such proce... Read More about Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics..

Walsh families of all rank-invariant classes of 3-bit pseudo-Boolean functions. [Dataset] (2016)
Data
CHRISTIE, L.A. 2016. Walsh families of all rank-invariant classes of 3-bit pseudo-Boolean functions. [Dataset]

This dataset was compiled as part of the following PhD thesis: CHRISTIE, L.A. 2016. The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance. Robert Gordon University, PhD thesis. H... Read More about Walsh families of all rank-invariant classes of 3-bit pseudo-Boolean functions. [Dataset].

Generating easy and hard problems using the proximate optimality principle. [Dataset] (2015)
Data
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. [Dataset]

These data were gathered to investigate the hypothesis that coherent functions will be easy and anti-coherent functions will be hard for a hillclimber. We generated 10 coherent functions for each length on bit-strings of length 6-100 and the same num... Read More about Generating easy and hard problems using the proximate optimality principle. [Dataset].