All Outputs (21)

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Conference Proceeding
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (in press). Understanding fitness landscapes in morpho-evolution via local optima networks. . https://doi.org/10.1145/3638529.3654059

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)
Conference Proceeding
Rodriguez, C. J., Thomson, S. L., Alderliesten, T., & Bosman, P. A. N. (in press). Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective. . https://doi.org/10.1145/3638529.3654125

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)
Conference Proceeding
Richter, H., & Thomson, S. L. (in press). Information flow and Laplacian dynamics on local optima networks.

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.

Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances (2024)
Conference Proceeding
Verel, S., Thomson, S. L., & Rifki, O. (in press). Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances.

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.

Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. (2023)
Conference Proceeding
Verduin, K., Thomson, S. L., & van den Berg, D. (2023). Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. In Proceedings of the 15th International Joint Conference on Computational Intelligence (246-257). https://doi.org/10.5220/0012192100003595

Unlike other NP-hard problems, the constraints on the traveling tournament problem are so pressing that it’s hardly possible to randomly generate a valid solution, for example, to use in a genetic algorithm’s initial population. In this study, we ran... Read More about Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem..

The Opaque Nature of Intelligence and the Pursuit of Explainable AI (2023)
Conference Proceeding
Thomson, S. L., van Stein, N., van den Berg, D., & van Leeuwen, C. (2023). The Opaque Nature of Intelligence and the Pursuit of Explainable AI. In Proceedings of the 15th International Joint Conference on Computational Intelligence (555-564). https://doi.org/10.5220/0012249500003595

When artificial intelligence is used for making decisions, people are more likely to accept those decisions if they can be made intelligible to the public. This understanding has led to the emerging field of explainable artificial intelligence. We re... Read More about The Opaque Nature of Intelligence and the Pursuit of Explainable AI.

Can HP-protein folding be solved with genetic algorithms? Maybe not (2023)
Conference Proceeding
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 (131-140). https://doi.org/10.5220/0012248500003595

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.

From Fitness Landscapes to Explainable AI and Back (2023)
Conference Proceeding
Thomson, S. L., Adair, J., Brownlee, A. E. I., & van den Berg, D. (2023). From Fitness Landscapes to Explainable AI and Back. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (1663-1667). https://doi.org/10.1145/3583133.3596395

We consider and discuss the ways in which search landscapes might contribute to the future of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to gain insight into algorithm search dynamics on optimisation prob... Read More about From Fitness Landscapes to Explainable AI and Back.

Randomness in Local Optima Network Sampling (2023)
Conference Proceeding
Thomson, S. L., Veerapen, N., Ochoa, G., & van den Berg, D. (2023). Randomness in Local Optima Network Sampling. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (2099-2107). https://doi.org/10.1145/3583133.3596309

We consider statistical randomness in the construction of local optima networks (LONs) and conduct a preliminary and exploratory study into this. LONs capture a fitness landscape into network format: the nodes are local optima, and edges are heuristi... Read More about Randomness in Local Optima Network Sampling.

Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality (2023)
Conference Proceeding
Graham, K. C., Thomson, S. L., & Brownlee, A. E. I. (2023). Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality. . https://doi.org/10.1145/3583133.3596433

We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for the Alpine 1 function as an exploratory and preliminary case study. Performance trends are analysed and the tuned population size for PSO across dimensi... Read More about Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality.

Channel Configuration for Neural Architecture: Insights from the Search Space (2023)
Conference Proceeding
Thomson, S. L., Ochoa, G., Veerapen, N., & Michalak, K. (2023). Channel Configuration for Neural Architecture: Insights from the Search Space. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference (1267-1275). https://doi.org/10.1145/3583131.3590386

We consider search spaces associated with neural network channel configuration. Architectures and their accuracy are visualised using low-dimensional Euclidean embedding (LDEE). Optimisation dynamics are captured using local optima networks (LONs). L... Read More about Channel Configuration for Neural Architecture: Insights from the Search Space.

Frequency Fitness Assignment on JSSP: A Critical Review (2023)
Conference Proceeding
de Bruin, E., Thomson, S. L., & Berg, D. V. D. (2023). Frequency Fitness Assignment on JSSP: A Critical Review. In J. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation: 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (351-363). https://doi.org/10.1007/978-3-031-30229-9_23

Metaheuristic navigation towards rare objective values instead of good objective values: is it a good idea? We will discuss the closed and open ends after presenting a successful replication study of Weise et al.’s ‘frequency fitness assignment’ for... Read More about Frequency Fitness Assignment on JSSP: A Critical Review.

Universally Hard Hamiltonian Cycle Problem Instances (2022)
Conference Proceeding
Sleegers, J., Thomson, S. L., & van den Berg, D. (2022). Universally Hard Hamiltonian Cycle Problem Instances. In T. Bäck, B. van Stein, C. Wagner, J. Garibaldi, H. Lam, M. Cottrell, …J. Kacprzyk (Eds.), Proceedings of the 14th International Joint Conference on Computational Intelligence (105-111). https://doi.org/10.5220/0011531900003332

In 2021, evolutionary algorithms found the hardest-known yes and no instances for the Hamiltonian cycle problem. These instances, which show regularity patterns, require a very high number of recursions for the best exact backtracking algorithm (Vand... Read More about Universally Hard Hamiltonian Cycle Problem Instances.

Fractal Dimension and Perturbation Strength: A Local Optima Networks View (2022)
Conference Proceeding
Thomson, S. L., Ochoa, G., & Verel, S. (2022). Fractal Dimension and Perturbation Strength: A Local Optima Networks View. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I (562-574). https://doi.org/10.1007/978-3-031-14714-2_39

We study the effect of varying perturbation strength on the fractal dimensions of Quadratic Assignment Problem (QAP) fitness landscapes induced by iterated local search (ILS). Fitness landscapes are represented as Local Optima Networks (LONs), which... Read More about Fractal Dimension and Perturbation Strength: A Local Optima Networks View.

On funnel depths and acceptance criteria in stochastic local search (2022)
Conference Proceeding
Thomson, S. L., & Ochoa, G. (2022). On funnel depths and acceptance criteria in stochastic local search. In GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference (287-295). https://doi.org/10.1145/3512290.3528831

We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic... Read More about On funnel depths and acceptance criteria in stochastic local search.

The Local Optima Level in Chemotherapy Schedule Optimisation (2020)
Conference Proceeding
Thomson, S. L., & Ochoa, G. (2020). The Local Optima Level in Chemotherapy Schedule Optimisation. In Evolutionary Computation in Combinatorial Optimization: 20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings (197-213). https://doi.org/10.1007/978-3-030-43680-3_13

In this paper a multi-drug Chemotherapy Schedule Optimisation Problem (CSOP) is subject to Local Optima Network (LON) analysis. LONs capture global patterns in fitness landscapes. CSOPs have not previously been subject to fitness landscape analysis.... Read More about The Local Optima Level in Chemotherapy Schedule Optimisation.

Clarifying the Difference in Local Optima Network Sampling Algorithms (2019)
Conference Proceeding
Thomson, S. L., Ochoa, G., & Verel, S. (2019). Clarifying the Difference in Local Optima Network Sampling Algorithms. In Evolutionary Computation in Combinatorial Optimization. EvoCOP 2019 (163-178). https://doi.org/10.1007/978-3-030-16711-0_11

We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. These methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on... Read More about Clarifying the Difference in Local Optima Network Sampling Algorithms.

Multifractality and dimensional determinism in local optima networks (2018)
Conference Proceeding
Thomson, S. L., Verel, S., Ochoa, G., Veerapen, N., & Cairns, D. (2018). Multifractality and dimensional determinism in local optima networks. In GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference (371-378). https://doi.org/10.1145/3205455.3205472

We conduct a study of local optima networks (LONs) in a search space using fractal dimensions. The fractal dimension (FD) of these networks is a complexity index which assigns a non-integer dimension to an object. We propose a fine-grained approach t... Read More about Multifractality and dimensional determinism in local optima networks.

On the Fractal Nature of Local Optima Networks (2018)
Conference Proceeding
Thomson, S. L., Verel, S., Ochoa, G., Veerapen, N., & McMenemy, P. (2018). On the Fractal Nature of Local Optima Networks. In Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018 (18-33). https://doi.org/10.1007/978-3-319-77449-7_2

A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. An object... Read More about On the Fractal Nature of Local Optima Networks.

The effect of landscape funnels in QAPLIB instances (2017)
Conference Proceeding
Thomson, S. L., Ochoa, G., Daolio, F., & Veerapen, N. (2017). The effect of landscape funnels in QAPLIB instances. In GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion (1495-1500). https://doi.org/10.1145/3067695.3082512

The effectiveness of common metaheuristics on combinatorial optimisation problems can be limited by certain characteristics of the fitness landscape. We use the local optima network model to compress the 'inherent structure' of a problem space into a... Read More about The effect of landscape funnels in QAPLIB instances.