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AdaGEP - An Adaptive Gene Expression Programming Algorithm
Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007), 2007Many papers focused on fine-tunning the gene expression programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size).
Elena Bautu, Andrei Bautu, Henri Luchian
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Gene Expression Programming without Reduplicate Individuals
2009 Fifth International Conference on Natural Computation, 2009The diversity plays an important role in gene expression programming(GEP). However, the reduplicate individuals in the populations decrease the diversity, which will impact the performance of the evolution. To cope with this problem, this paper proposes a novel GEP WithOut Reduplicate Individuals called GEPWORI.
Taiyong Li +4 more
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Evolving Classifier Ensemble With Gene Expression Programming
Third International Conference on Natural Computation (ICNC 2007), 2007Gene expression programming (GEP) is a kind of geno-type/phenotype based evolutionary computation(EC) algorithm. GEP has been successfully applied in data mining (DM) fields such as regression, classification and association rules mining. Although GEP has been used as a raw DM tool in these fields, its potential to combine with DM techniques has not ...
Qu Li +3 more
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Gene expression programs in mammalian spermatogenesis
DevelopmentABSTRACT Mammalian spermatogenesis, probably the most complex of all cellular developmental processes, is an ideal model both for studying the specific mechanism of gametogenesis and for understanding the basic rules governing all developmental processes, as it entails both cell type-specific and housekeeping molecular processes ...
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The Gene Expression Programming Applied to Demand Forecast
2010This paper examines the use of artificial intelligence (in particular the aplication of Gene Expression Programming, GEP) to demand forecasting. In the world of production management, many data that are produced in function of the of economic activity characteristics in which they belong, may suffer, for example, significant impacts of seasonal ...
Evandro Bittencourt +4 more
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Programming of gene expression by Polycomb group proteins
Trends in Cell Biology, 2008Polycomb group (PcG) complexes maintain epigenetically repressed states that need to be reprogrammed when cells become committed to differentiation. In contrast to the previously held belief that PcG complexes regulate only a few selected genes, recent efforts have revealed hundreds of potential PcG targets in mammals, insects and plants. These results
Köhler, C., Villar, C.
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Multi-Expression Based Gene Expression Programming
2013Among the variants of GP, GEP stands out for its simplicity of encoding method and MEP catches our attention for its multi-expression capability. In this paper, a novel GP variant-MGEP (Multi-expression based Gene Expression Programming) is proposed to combine these two approaches.
Wei Deng, Pei He, Zhi Huang
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Cellular Gene Expression Programming Classifier Learning
2011In this paper we propose integrating two collective computational intelligence techniques: gene expression programming and cellular evolutionary algorithms with a view to induce expression trees, which, subsequently, serve as weak classifiers. From these classifiers stronger ensemble classifiers are constructed using majority-voting and boosting ...
Joanna Jedrzejowicz, Piotr Jedrzejowicz
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Distance Guided Classification with Gene Expression Programming
2006Gene Expression Programming (GEP) aims at discovering essential rules hidden in observed data and expressing them mathematically. GEP has been proved to be a powerful tool for constructing efficient classifiers. Traditional GEP-classifiers ignore the distribution of samples, and hence decrease the efficiency and accuracy.
Lei Duan +4 more
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Gene Expression Programming and the Evolution of Computer Programs
2005In this chapter an artificial problem solver inspired in natural genotype/phenotype systems — gene expression programming — is presented. As an introduction, the fundamental differences between gene expression programming and its predecessors, genetic algorithms and genetic programming, are briefly summarized so that the evolutionary advantages of gene
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