Deconstructing GAs into Visual Software Components

Garzón-Rodriguez, Leidy Patricia; Diosa, Henry Alberto; Rojas–Galeano, Sergio

doi:10.1145/2739482.2768466

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Some of them are targeted to specific domains, such as urban economic modeling, vehicle routing problems, multiple sequence alignment, hydrologic model calibration, and project scheduling . Other libraries cover different search methods and implementation issues, from a broader scope of heuristic algorithms, nature‐inspired metaheuristics, memetic algorithms, local search metaheuristics, and visualization of genetic algorithm components …”

Section: Software Design Support For Heuristic Algorithmsmentioning

confidence: 99%

“…18 Other libraries cover different search methods and implementation issues, from a broader scope of heuristic algorithms, 10,19 nature-inspired metaheuristics, 20 memetic algorithms, 21 local search metaheuristics, 22 and visualization of genetic algorithm components. 23 Regarding Java-based libraries, several options for implementing heuristics and metaheuristics can be found in the literature. Opt4J 7 and jMetal 24 are two alternatives that offer interesting features and applications.…”

Section: Software Libraries and Toolsmentioning

confidence: 99%

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hMod: A software framework for assembling highly detailed heuristics algorithms

et al. 2019

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Summary Software design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod.

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Section: Software Design Support For Heuristic Algorithmsmentioning

confidence: 99%

Section: Software Libraries and Toolsmentioning

confidence: 99%

hMod: A software framework for assembling highly detailed heuristics algorithms

et al. 2019

View full text Add to dashboard Cite

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“…The method was implemented using the Goldenberry suite of visual components for stochastic–based search optimisation within the Orange multi–platform workbench for data mining [ 33 , 34 ]. In this environment, visual components (known as widgets ) executing different steps of the algorithm such as data input and sampling, SVM s training, BMDA estimation, etc., are dragged onto a visual canvas where they are assembled to create the Kiedra program shown in Fig.…”

Section: Empirical Studymentioning

confidence: 99%

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

Rodríguez

Rojas–Galeano

2017

BioData Mining

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BackgroundDiscovering relevant features (biomarkers) that discriminate etiologies of a disease is useful to provide biomedical researchers with candidate targets for further laboratory experimentation while saving costs; dependencies among biomarkers may suggest additional valuable information, for example, to characterize complex epistatic relationships from genetic data. The use of classifiers to guide the search for biomarkers (the so–called wrapper approach) has been widely studied. However, simultaneously searching for relevancy and dependencies among markers is a less explored ground.ResultsWe propose a new wrapper method that builds upon the discrimination power of a weighted kernel classifier to guide the search for a probabilistic model of simultaneous marginal and interacting effects. The feasibility of the method was evaluated in three empirical studies. The first one assessed its ability to discover complex epistatic effects on a large–scale testbed of generated human genetic problems; the method succeeded in 4 out of 5 of these problems while providing more accurate and expressive results than a baseline technique that also considers dependencies. The second study evaluated the performance of the method in benchmark classification tasks; in average the prediction accuracy was comparable to two other baseline techniques whilst finding smaller subsets of relevant features. The last study was aimed at discovering relevancy/dependency in a hepatitis dataset; in this regard, evidence recently reported in medical literature corroborated our findings. As a byproduct, the method was implemented and made freely available as a toolbox of software components deployed within an existing visual data–mining workbench.ConclusionsThe mining advantages exhibited by the method come at the expense of a higher computational complexity, posing interesting algorithmic challenges regarding its applicability to large–scale datasets. Extending the probabilistic assumptions of the method to continuous distributions and higher–degree interactions is also appealing. As a final remark, we advocate broadening the use of visual graphical software tools as they enable biodata researchers to focus on experiment design, visualisation and data analysis rather than on refining their scripting programming skills.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-017-0131-y) contains supplementary material, which is available to authorized users.

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A systematic literature review of modern software visualization

Chotisarn

Merino

et al. 2020

J Vis

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Deconstructing GAs into Visual Software Components

Cited by 4 publications

References 10 publications

hMod: A software framework for assembling highly detailed heuristics algorithms

hMod: A software framework for assembling highly detailed heuristics algorithms

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

A systematic literature review of modern software visualization

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