Results 231 to 240 of about 56,733 (259)

Comparison of Genetic Programming, Grammatical Evolution and Gene Expression Programming Techniques

2014
The purpose of this paper is to compare the efficiency of three different evolutionary programming techniques – Genetic Programming, Grammatical Evolution and Gene Expression Programming. These algorithms were applied to different type test problems with the same set of parameters. The results of the experiments and some insights on similar experiments
Evaldas Guogis, Alfonsas Misevicius
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A Genetic Representation for Dynamic System Qualitative Models on Genetic Programming: A Gene Expression Programming Approach

2007
In this work we design a genetic representation and its genetic operators to encode individuals for evolving Dynamic System Models in a Qualitative Differential Equation form, for System Identification. The representation proposed, can be implemented in almost every programming language without the need of complex data structures, this representation ...
Ramiro Serrato Paniagua, Juan J. Flores, Carlos A. Coello Coello   +2 more
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Active learning approaches for learning regular expressions with genetic programming

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016
We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behavior. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system.
BARTOLI, Alberto, DE LORENZO, ANDREA, MEDVET, Eric, TARLAO, FABIANO   +3 more
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An Encoding Scheme for Generating λ-Expressions in Genetic Programming

2003
To apply genetic programming (GP) to evolve λ-expressions, we devised an encoding scheme that encodes λ-expressions into trees. This encoding has closure property, i.e., any combination of terminal and non-terminal symbols forms a valid λ-expression. We applied this encoding to a simple symbolic regression problem over Church numerals and the objective
Kazuto Tominaga, Tomoya Suzuki, Kazuhiro Oka   +2 more
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Genetic programming with smooth operators for arithmetic expressions: diviplication and subdition

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2003
This paper introduces the smooth operators for arithmetic expressions as an approach to smoothening the search space in Genetic Programming (GP). Smooth operator GP interpolates between arithmetic operators such as * and /, thereby allowing a gradual adaptation to the problem.
Ursem, Rasmus Kjær, Krink, Thiemo
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Genetic expression programming: a new approach for QoS traffic prediction in EPONs

Photonic Network Communications, 2013
The Ethernet passive optical network is being regarded as the most promising for next-generation optical access solutions in the access networks. In time division multiplexing passive optical network technology (TDM-PON), the dynamic bandwidth allocation (DBA) plays a crucial key role to achieve efficient bandwidth allocation and fairness among ...
Jhong-Yue Lee, I-Shyan Hwang, Andrew Tanny Liem, K. Robert Lai, AliAkbar Nikoukar   +4 more
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A combination of kernel methods and genetic programming for gene expression pattern classification

2006 International Conference onResearch, Innovation and Vision for the Future, 2006
The rapidly emerging field of quantitative proteomics has established itself as a credible approach for understanding of the biology of whole organisms. Classification of proteins according to the level of their expression during a particular process allows discovering causal relationships among genes and proteins involved in the process. In this paper,
Cuong To, Jiri Vohradsky
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Classification of Gene Expression Data by Majority Voting Genetic Programming Classifier

2006 IEEE International Conference on Evolutionary Computation, 2006
Recently, genetic programming (GP) has been applied to the classification of gene expression data. In its typical implementation, using training data, a single rule or a single set of rules is evolved with GP, and then it is applied to test data to get generalized test accuracy. However, in most cases, the generalized test accuracy is not higher.
Topon Kumar Paul, Yoshihiko Hasegawa, Hitoshi Iba   +2 more
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A New Probabilistic Tree Expression for Probabilistic Model Building Genetic Programming

2019
This paper proposes a new expression of probabilistic tree for probabilistic model building GPs (PMBGP). Tree-structured PMBGPs estimate the probability of appearance of symbols at each node of the tree from past search information, and decide the symbol based on the probability at each node in generating a solution.
Daichi Kumoyama, Yoshiko Hanada, Keiko Ono   +2 more
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Conditionals Support in Binary Expression Tree Based Genetic Programming

2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech), 2022
Feng-Cheng Chang, Hsiang-Cheh Huang
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