Results 111 to 120 of about 77,914 (161)

Gene expression programming with multiple chromosomes

International Journal of Modelling, Identification and Control, 2011
Gene expression programming (GEP) has been widely used in the areas of pattern recognition and knowledge discovery, however, when dealing with complicated problems, it is very time-consuming and the number of generations is large. In order to overcome these drawbacks, this paper proposes a multi-chromosomes GEP algorithm (MC-GEP).
Min Yao
exaly   +2 more sources

Unconstrained gene expression programming

2009 IEEE Congress on Evolutionary Computation, 2009
Many linear structured genetic programming are proposed in the past years. Gene expression programming, as a classic linear represented genetic programming, is powerful in solving problems of data mining and knowledge discovery. Constrains of gene expression programming like head-tail mechanism do contribution to the legality of chromosome.
Jianwei Zhang, Zhijian Wu, Zongyue Wang, Jinglei Guo, Zhangcan Huang   +4 more
openaire   +1 more source

Programming gene expression in developing epidermis

Development, 1994
ABSTRACT As the major proteins of adult keratinocytes, keratins provide biochemical markers for exploring mouse epidermal embryogenesis. Here, we used a modified method of whole-mount in situ hybridization to track skin-specific expression of endogenous keratin mRNAs through-out embryogenesis.
C, Byrne, M, Tainsky, E, Fuchs
openaire   +2 more sources

Generating plants with gene expression programming

Proceedings of the 5th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa, 2007
The simulated evolution of botanical trees and other plants is explored in this paper. We present a model to simplify the creation of plants by following the genotype/phenotype approach of Gene Expression Programming to generate L-Systems. Our model describes a specific organism (a plant) with a genotype that can be expressed as an L-System.
Johannes Venter, Alexandre Hardy
openaire   +1 more source

Gene Expression Programming

2017
Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent
Baddrud Zaman Laskar, Swanirbhar Majumder   +1 more
openaire   +1 more source

Self-Learning Gene Expression Programming

IEEE Transactions on Evolutionary Computation, 2016
In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation in which each chromosome is embedded with subfunctions that can be deployed to ...
Jinghui Zhong, Yew-Soon Ong, Wentong Cai 0001   +2 more
openaire   +1 more source

Programmed Vesicle Fusion Triggers Gene Expression

Langmuir, 2011
The membrane properties of phospholipid vesicles can be manipulated to both regulate and initiate encapsulated biochemical reactions and networks. We present evidence for the inhibition and activation of reactions encapsulated in vesicles by the exogenous addition of charged amphiphiles.
Caschera, Filippo, Sunami, Takeshi, Matsuura, Tomoaki, Suzuki, Hiroaki, Hanczyc, Martin Michael, Yomo, Tetsuya   +5 more
openaire   +2 more sources

Gene expression programming in prediction

Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 2004
In order to solve the prediction problem of multiple variables, gene expression programming was used in comparison with genetic programming and linear regression in terms of accuracy and stability. Gene expression programming was chosen for its high performance and easy genetic manipulation comparing with genetic programming.
null Qu Li, null Zhihua Cai, null Siwei Jiang, null Li Zhu   +3 more
openaire   +1 more source

Gene Expression Programming with DAG Chromosome

2007
GEP(Gene Expression Programming) is applied to comprehensive fields such as Symbolic Regression, Parameter Optimization, Cellular Automate etc[2].With Kara-style chromosome, GEP can only express tree phynotype. This limits the expressiveness of the program that can be evolved.
Hui-yun Quan, Guangyi Yang
openaire   +1 more source

Gene Expression Programming: A Survey [Review Article]

IEEE Computational Intelligence Magazine, 2017
Abstract Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number of enhanced GEPs have been proposed to date and the real world applications that use them are also multiplying fast.
Jinghui Zhong, Liang Feng 0001, Yew-Soon Ong   +2 more
openaire   +1 more source