Abstract
The aim of this paper is to introduce a robust semantic to genetic learning behavior. The conceptualization of new genetic machine learning relies on the specification model of Entity-Relation. According to this perspective, the learning behavior is decomposed in two evolution dimensions. In addition to some added concepts, the model uses the learning metaphor of coarse adaptability. The configuration of the system leads to a decentralized architecture where entities and interactions provide more reality to the overall genetic machinery. Based upon an adaptation function, the learning put emphasizes on adjustments of mutation rates through generations. This landscape draws the best strategy to control the intensity of convergence velocity along of evolution.
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Ali, Y.M.B., Laskri, M.T. (2004). Robust Semantic for an Evolved Genetic Algorithm-Based Machine Learning. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_40
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DOI: https://doi.org/10.1007/978-3-540-24840-8_40
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