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Outputs (30)

Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances (2024)
Presentation / Conference Contribution
Verel, S., Thomson, S. L., & Rifki, O. (2024, April). Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances. Presented at EvoCOP 2024

The Quadratic Assignment Problem (QAP) is one of the major domains in the field of evolutionary computation, and more widely in combinatorial optimization. This paper studies the phase transition of the QAP, which can be described as a dramatic chang... Read More about Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances.

Explaining evolutionary feature selection via local optima networks (2024)
Presentation / Conference Contribution
Adair, J., Thomson, S. L., & Brownlee, A. E. (2024, July). Explaining evolutionary feature selection via local optima networks. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We analyse fitness landscapes of evolutionary feature selection to obtain information about feature importance in supervised machine learning. Local optima networks (LONs) are a compact representation of a landscape, and can potentially be adapted fo... Read More about Explaining evolutionary feature selection via local optima networks.

Exploring the use of fitness landscape analysis for understanding malware evolution (2024)
Presentation / Conference Contribution
Babaagba, K., Murali, R., & Thomson, S. L. (2024, July). Exploring the use of fitness landscape analysis for understanding malware evolution. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis.... Read More about Exploring the use of fitness landscape analysis for understanding malware evolution.

Frequency Fitness Assignment for Untangling Proteins in 2D (2024)
Presentation / Conference Contribution
Koutstaal, J., Kommandeur, J., Timmer, R., Horn, R., Thomson, S. L., & van den Berg, D. (2024, April). Frequency Fitness Assignment for Untangling Proteins in 2D. Presented at EvoStar 2024, Aberyswyth, UK

At the time of writing, there is no known deterministic-time algorithm to sample valid initial solutions with uniform random distribution for the HP protein folding model, because guaranteed uniform random sampling produces collisions (i.e. constrain... Read More about Frequency Fitness Assignment for Untangling Proteins in 2D.

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Presentation / Conference Contribution
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (2024, July). Understanding fitness landscapes in morpho-evolution via local optima networks. Presented at Genetic and Evolutionary Computation Conference (GECCO 2024), Melbourne, Australia

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Pre... Read More about Understanding fitness landscapes in morpho-evolution via local optima networks.

Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective (2024)
Presentation / Conference Contribution
Rodriguez, C. J., Thomson, S. L., Alderliesten, T., & Bosman, P. A. N. (2024, July). Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective. Presented at Genetic and Evolutionary Computation C

Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes... Read More about Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective.

Information flow and Laplacian dynamics on local optima networks (2024)
Presentation / Conference Contribution
Richter, H., & Thomson, S. L. (2024, June). Information flow and Laplacian dynamics on local optima networks. Presented at IEEE Congress on Evolutionary Computation (IEEE CEC), Yokohama, Japan

We propose a new way of looking at local optima networks (LONs). LONs represent fitness landscapes; the nodes are local optima, and the edges are search transitions between them. Many metrics computed on LONs have been proposed and shown to be linked... Read More about Information flow and Laplacian dynamics on local optima networks.

Can HP-protein folding be solved with genetic algorithms? Maybe not (2023)
Presentation / Conference Contribution
Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023). Can HP-protein folding be solved with genetic algorithms? Maybe not. In Proceedings of the 15th International Joint Conference on Computational Intelligence (131-1

Genetic algorithms might not be able to solve the HP-protein folding problem because creating random individuals for an initial population is very hard, if not impossible. The reason for this, is that the expected number of constraint violations incr... Read More about Can HP-protein folding be solved with genetic algorithms? Maybe not.