To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes

Schäpermeier, Lennart; Grimme, Christian; Kerschke, Pascal

doi:10.1007/978-3-030-72062-9_50

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Several other tools have been developed for displaying performance data and/or the search behavior in decision space. However, all tools that we are aware of allow much less lexibility with respect to the performance measures, the ranges, and the granularity of the analysis or focus on selected aspects of performance analysis only (e.g., [5,17] study statistical signiicance, whereas [20,53] aim to visualize performance with respect to multiple objectives). The ability of IOHanalyzer to link the evolution of algorithms' parameters to the evolution of solutions' quality seems to be unique.…”

Section: Related Benchmarking Environmentsmentioning

confidence: 99%

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

Wang

Vermetten

et al. 2022

ACM Trans. Evol. Learn. Optim.

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Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, etc. This task, however, involves manual setup, execution, and analysis of the experiment on an individual basis, which is laborious and can be mitigated by a generic and well-designed platform. For this purpose, we propose IOHanalyzer, a new user-friendly tool for the analysis, comparison, and visualization of performance data of IOHs. Implemented in R and C++ , IOHanalyzer is fully open source. It is available on CRAN and GitHub. IOHanalyzer provides detailed statistics about fixed-target running times and about fixed-budget performance of the benchmarked algorithms with a real-valued codomain, single-objective optimization tasks. Performance aggregation over several benchmark problems is possible, for example in the form of empirical cumulative distribution functions. Key advantages of IOHanalyzer over other performance analysis packages are its highly interactive design, which allows users to specify the performance measures, ranges, and granularity that are most useful for their experiments, and the possibility to analyze not only performance traces, but also the evolution of dynamic state parameters. IOHanalyzer can directly process performance data from the main benchmarking platforms, including the COCO platform, Nevergrad, the SOS platform, and IOHexperimenter. An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.

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Section: Related Benchmarking Environmentsmentioning

confidence: 99%

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

Wang

Vermetten

et al. 2022

ACM Trans. Evol. Learn. Optim.

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show abstract

“…This approach, however, does not capture local optimal sets. Kerschke and Grimme [8] proposed the gradient field maps to explicitly address local optimal sets, with further extensions plotting landscapes with optimal trade-offs [16], and providing an accessible dashboard for visualization [17]. In combinatorial optimization, recent work has adapted the local optima networks model [13] to multiobjective optimization, providing visual insights into the distribution and connectivity pattern of Pareto local optimal solutions [11] and dominance-based hill-climbing [6].…”

Section: Related Workmentioning

confidence: 99%

Search Trajectories Networks of Multiobjective Evolutionary Algorithms

Lavinas¹,

Aranha²,

Ochoa³

2022

Preprint

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Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis.

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“…In an attempt to consolidate recent developments in MOP visualization and to facilitate the use of the underlying techniques, Schäpermeier et al (2021) recently published a user-friendly dashboard that enables interactive and platform-independent exploration of MOP landscapes. 3 The dashboard also provides two methods for interactively exploring three-dimensional search spaces of MOPs: one based on the idea of MRI scans (see Fig.…”

Section: Visualization Of Multi-objective Landscapesmentioning

confidence: 99%

Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization

Grimme

Kerschke

Aspar

et al. 2021

Computers & Operations Research

Self Cite

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To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes

Cited by 14 publications

References 22 publications

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

Search Trajectories Networks of Multiobjective Evolutionary Algorithms

Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization

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