EAVis: A Visualization Tool for Evolutionary Algorithms

Kerren, Andreas; Egger, Thomas

doi:10.1109/vlhcc.2005.33

Cited by 16 publications

(10 citation statements)

References 3 publications

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“…Visualization is a powerful tool for supporting reasoning and is often used to gain insight into the workings of Evolutionary Algorithms (EAs). Over the years, several visualization tools for the analysis of EAs have been proposed [8,12,18,22,23]. They generally provide exploration of EAs properties at two levels, that of single solutions (heavily dependent on solution representation) and entire populations (mostly showing how the fitness improves over generations).…”

Section: Introductionmentioning

confidence: 99%

Unveiling evolutionary algorithm representation with DU maps

Medvet

Virgolin

Castelli

et al. 2018

Genet Program Evolvable Mach

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Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly available to all users, or too general to convey detailed information. In this work, we study the Diversity and Usage map (DU map), a compact visualization for analyzing a key component of every EA, the representation of solutions. In a single heat map, the DU map visualizes for entire runs how diverse the genotype is across the population and to which degree each gene in the genotype contributes to the solution. We demonstrate the generality of the DU map concept by applying it to six EAs that use different representations (bit and integer strings, trees, ensembles of trees, and neural networks). We present the results of an online user study about the usability of the DU map which confirm the suitability of the proposed tool and provide important insights on our design choices. By providing a visualization tool that can be easily tailored by specifying the diversity (D) and usage (U) functions, the DU map aims at being a powerful analysis tool for EAs practitioners, making EAs more transparent and hence lowering the barrier for their use.

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Section: Introductionmentioning

confidence: 99%

Unveiling evolutionary algorithm representation with DU maps

Medvet

Virgolin

Castelli

et al. 2018

Genet Program Evolvable Mach

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“…Another work is that of [7], where in their visualization package EAVis the authors used a so-called evolution map that "holds various information about the evolution of a whole system over the time". The map is comprised of lines (representing mutations), boxes (inversion) and circles (crossover) positioned at random.…”

Section: Previous Work In Ea Visualiza-tionmentioning

confidence: 99%

EA stability visualization

Craven

Claver

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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It is well-known that Evolutionary Algorithms (EAs) are sensitive to changes in their control parameters, and it is generally agreed that too large a change may turn the EA from being successful to unsuccessful. This work reports on an experimental hybrid visualization scheme for the determination of EA stability according to perturbation of EA parameters. The scheme gives a visual representation of local neighborhoods of the parameter space according to a choice of two perturbation metrics, relating perturbations to EA performance as a variant of Kolmogorov distance. Through visualization and analysis of twelve thousand case study EA runs, we illustrate that we are able to distinguish between EA stability and instability depending upon perturbation and performance metrics. Finally we use what we have learnt in the case study to provide a methodology for more general EAs.

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“…There do exist several other tools designed for analysis and visualization of genetic algorithms [9] [17]. One such tool is GAVEL [9], which provides a visualization of the alleles and genes produced by a given genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

EVEVis: A multi-scale visualization system for dense evolutionary data

Miller

Mozhayskiy

Tagkopoulos

et al. 2011

2011 IEEE Symposium on Biological Data Visualization (BioVis).

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Evolutionary simulations can produce datasets consisting of thousands or millions of separate entities, complete with their genealogical relationships. Biologists must examine this data to determine when and where these entities have changed, both on an individual basis and on a population-wide basis. Therefore, desirable features of a visualization system for evolutionary data are the capability of showing the status of the population at any given moment in time, good scalability, and smooth transition between high-level and lowlevel views. We propose a multi-scale visualization method, including a novel tree layout that both shows population status over time and can easily scale to very large populations. From this layout, the user can navigate to visualizations for moments in time or for individual entities. We demonstrate the effectiveness of the visualization on an existing evolutionary simulation called EVE: Evolution in Variable Environments.

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EAVis: A Visualization Tool for Evolutionary Algorithms

Cited by 16 publications

References 3 publications

Unveiling evolutionary algorithm representation with DU maps

Unveiling evolutionary algorithm representation with DU maps

EA stability visualization

EVEVis: A multi-scale visualization system for dense evolutionary data

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