Results 241 to 250 of about 208,088 (288)

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   +3 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

Cellular gene expression programming classifier learning [PDF]

, 2011
In 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 Jędrzejowicz, Piotr Jędrzejowicz   +1 more
  +4 more sources

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

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

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
openaire   +1 more source

Distribution-Estimation Gene Expression Programming

2010 International Conference on Computational Intelligence and Software Engineering, 2010
This paper presents a new form of gene expression programming based on distribution-estimation model with tree structure (TS) and graph structure (GS); The results of experiments indicates that the proposed approach achieves a good performance and EDGEP are effective in max problem and 6 multiplexer problem.
Yanyun Tao, Wenhao Yuan, Jiajun Lin, Minglu Li   +3 more
openaire   +1 more source

Multi-Expression Based Gene Expression Programming

2013
Among 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
openaire   +1 more source

Gene Expression Programming without Reduplicate Individuals

2009 Fifth International Conference on Natural Computation, 2009
The 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, Changjie Tang, Ting He, Jiang Wu, Wenbing Qin   +4 more
openaire   +1 more source