Journal ArticleDOI
JCLEC: a Java framework for evolutionary computation
Sebastián Ventura,Cristóbal Romero,Amelia Zafra,José Antonio Sanz Delgado,César Hervás +4 more
- Vol. 12, Iss: 4, pp 381-392
TLDR
JCLEC, a Java software system for the development of evolutionary computation applications, has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort.Abstract:
In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.read more
Citations
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Proceedings Article
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
Jesús Alcalá-Fdez,Alberto Fernández,Julián Luengo,Joaquín Derrac,Salvador García,Luciano Sánchez,Francisco Herrera +6 more
TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.
Journal ArticleDOI
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Jesús Alcalá-Fdez,Luciano Sánchez,Salvador García,M. J. del Jesus,Sebastián Ventura,Josep Maria Garrell,José Otero,Cristóbal Romero,Jaume Bacardit,Víctor M. Rivas,Juan Carlos Fernández,Francisco Herrera +11 more
TL;DR: KEEL as discussed by the authors is a software tool to assess evolutionary algorithms for data mining problems of various kinds including regression, classification, unsupervised learning, etc., which includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL.
Journal ArticleDOI
Multiple instance learning: A survey of problem characteristics and applications
TL;DR: A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.
Journal ArticleDOI
Participation-based student final performance prediction model through interpretable Genetic Programming
TL;DR: This paper synthesizes learning analytics approaches, educational data mining (EDM) and HCI theory to explore the development of more usable prediction models and prediction model representations using data from a collaborative geometry problem solving environment: Virtual Math Teams with Geogebra (VMTwG).
Proceedings ArticleDOI
Opt4J: a modular framework for meta-heuristic optimization
TL;DR: A modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately by enabling a maximal decoupling and flexibility.
References
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Journal ArticleDOI
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Rainer Storn,Kenneth Price +1 more
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