Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data-intensive computing for competent genetic algorithms: a pilot study using meandre
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Simulink [8] has become a reference framework to accurately design, simulate, implement, and test control, signal processing, communications, and other time-varying systems. A key element of its success is a simple visual programming approach. It allows users to create targeted designs by reusing basic building-block functionalities and graphically creating a flow of execution among them. Users just need to focus on the target problem, not being disturbed by implementation details. Few attempts have been conducted in the evolutionary computation community to provide such a framework that allows beginners and researchers to share a common ground that allow them to deploy and quickly reuse the basic components of evolutionary algorithms. Evolvica [1] was a first effort to push toward this endeavor. Developed at Technical University of Ilmenau, it provided a first attempt to create a visual programming oriented framework. The last Evolvica release dates back to July 2004. Other tools such as ECJ [7] and Open BEAGLE [2] have been more oriented to cover researchers' needs than to create such an integration framework.