Visualising Evolution History in Multi- and Many-objective Optimisation

Walter, Mathew J.; Walker, David J.; Craven, Matthew J.

doi:10.1007/978-3-030-58115-2_21

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2025

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Cited by 3 publications

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“…principal component analysis) (see e.g. [6] for this). However, also for these approaches, identifying relationships quickly becomes more difficult as the dimensionality increases.…”

Section: Introductionmentioning

confidence: 99%

A Visualizable Test Problem Generator for Many-Objective Optimization

Fieldsend

Chugh

Allmendinger

et al. 2022

IEEE Trans. Evol. Computat.

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“…principal component analysis) (see e.g. [6] for this). However, also for these approaches, identifying relationships quickly becomes more difficult as the dimensionality increases.…”

Section: Introductionmentioning

confidence: 99%

A Visualizable Test Problem Generator for Many-Objective Optimization

Fieldsend

Chugh

Allmendinger

et al. 2022

IEEE Trans. Evol. Computat.

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Visualizing Population Dynamics to Examine Algorithm Performance

Walter

Walker

Craven

2022

IEEE Trans. Evol. Computat.

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This work assesses the efficacy of evolutionary algorithms (EAs) using an intuitive Multi-Dimensional Scaling (MDS) visualisation of the evolution of a population. We propose the use of Landmark Multi-Dimensional Scaling (LMDS) to overcome computational challenges inherent to visualising many-objective and complex problems with MDS. For the benchmark problems we tested, LMDS is akin to MDS visually, whilst requiring less than 1% of the time and memory necessary to produce an MDS visualisation of the same objective space solutions, leading to the possibility of online visualisations for multiand many-objective optimisation evaluation. Using multi-and many-objective problems from the DTLZ and WFG benchmark test suites, we analyse how Landmark MDS visualisations can offer far greater insight into algorithm performance than using traditional algorithm performance metrics such as hypervolume alone, and can be used to complement explicit performance metrics. Ultimately, this visualisation allows visual identification of problem features and assists the decision maker in making intuitive recommendations for algorithm parameters/operators for creating and testing better EAs to solve multi-and manyobjective problems.

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