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Learning expressive linkage rules for entity matching using genetic programming.
, 2013 openaire +2 more sources
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A genetic programming-based feature selection and fusion for facial expression recognition
Applied Soft Computing Journal, 2021Abstract Emotion recognition has become one of the most active research areas in pattern recognition due to the emergence of human–machine interaction systems. Describing facial expression is a very challenging problem since it relies on the quality of the face representation.
Haythem Ghazouani
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Genetic programming and gene expression programming for flyrock assessment due to mine blasting
International Journal of Rock Mechanics and Minings Sciences, 2016This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs
Edy Tonnizam Mohamad +2 more
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Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy
Expert Systems With Applications, 2011Liquefaction of soils induced by the earthquake is one of the major complex problems for the geotechnical engineering. It is generally determined from in situ tests and laboratory test of which application is very difficult, expensive and time consuming. They also require extreme cautions and labor.
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Functional genetic programming and exhaustive program search with combinator expressions [PDF]
International Journal of Knowledge-based and Intelligent Engineering Systems, 2008 Using a strongly typed functional programming language for genetic programming has many advantages, but evolving functional programs with variables requires complex genetic operators with special cases to avoid creating ill-formed programs. We introduce combinator expressions as an alternative program representation for genetic programming, providing ...
Forrest Briggs, Melissa E. O'Neill
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Full-Reference Image Quality Expression via Genetic Programming
IEEE Transactions on Image Processing, 2023Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we ...
Illya Bakurov +4 more
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ME-CGP: Multi Expression Cartesian Genetic Programming
IEEE Congress on Evolutionary Computation, 2010Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. In this paper we propose a way of structuring a CGP algorithm to make use of the multiple phenotypes which are implicitly encoded in a genome string.
Phil T. Cattani, Colin G. Johnson
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Expressive Genetic Programming
Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2013The language in which evolving programs are expressed can have significant impacts on the problem-solving capabilities of a genetic programming system. These impacts stem both from the absolute computational power of the languages that are used, as elucidated by formal language theory, and from the ease with which various computational structures can ...
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A Multiple Expression Alignment Framework for Genetic Programming
2018Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution ...
Leonardo Vanneschi +2 more
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