Relevant bibliographies by topics / Multi expression programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Multi expression programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Multi expression programming"

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Chen, Long Bin, and Pei He. "Multi-Subexpression Programming." Applied Mechanics and Materials 411-414 (September 2013): 2067–73. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2067.

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Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best f
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Sharifi, Sohrab, Saeed Abrishami, and Amir H. Gandomi. "Consolidation assessment using Multi Expression Programming." Applied Soft Computing 86 (January 2020): 105842. http://dx.doi.org/10.1016/j.asoc.2019.105842.

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Al-Saati, Najla, and Taghreed Alreffaee. "Software Effort Estimation Using Multi Expression Programming." AL-Rafidain Journal of Computer Sciences and Mathematics 11, no. 2 (2014): 53–71. http://dx.doi.org/10.33899/csmj.2014.163756.

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Dioşan, Laura, and Anca Andreica. "Multi-objective breast cancer classification by using multi-expression programming." Applied Intelligence 43, no. 3 (2015): 499–511. http://dx.doi.org/10.1007/s10489-015-0668-8.

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Li, Qu, Hongbing Cheng, and Min Yao. "Adaptive Multi-phenotype Based Gene Expression Programming Algorithm." Chinese Journal of Electronics 25, no. 5 (2016): 807–16. http://dx.doi.org/10.1049/cje.2016.08.041.

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NI, Sheng-qiao, Chang-jie TANG, Ning YANG, and Jie ZUO. "Gene expression programming algorithm based on multi-threading evaluator." Journal of Computer Applications 32, no. 4 (2013): 986–89. http://dx.doi.org/10.3724/sp.j.1087.2012.00986.

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Baykasoğlu, Adil, and Lale Özbakir. "MEPAR-miner: Multi-expression programming for classification rule mining." European Journal of Operational Research 183, no. 2 (2007): 767–84. http://dx.doi.org/10.1016/j.ejor.2006.10.015.

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Ma, Xin, and Zhi-bin Liu. "Predicting the Oil Well Production Based on Multi Expression Programming." Open Petroleum Engineering Journal 9, no. 1 (2016): 21–32. http://dx.doi.org/10.2174/1874834101609010021.

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Predicting the oil well production is very important and also quite a complex mission for the petroleum engineering. Due to its complexity, the previous empirical methods could not perform well for different kind of wells, and intelligent methods are applied to solve this problem. In this paper the multi expression programming (MEP) method has been employed to build the prediction model for oil well production, combined with the phase space reconstruction technique. The MEP has shown a better performance than the back propagation networks, gene expression programming method and the Arps declin
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Wang, Xue Chen, Xiao Guang Yue, Qing Guo Ren, and Zi Qiang Zhao. "Research on Coal Mine Rescue Robot Model." Applied Mechanics and Materials 340 (July 2013): 801–4. http://dx.doi.org/10.4028/www.scientific.net/amm.340.801.

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According to the situation of frequently domestic mining safety accidents, the basic theory and related concepts of bioinformatics' gene expression programming and multi-agent system are discussed. Related concepts of Bioinformatics and biological evolution and evolutionary computation are described in this paper. A coal mine rescue robot working model is discussed based on bioinformatics gene expression programming algorithm and multi-agent system theory.
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Alavi, Amir Hossein, Amir Hossein Gandomi, Mohammad Ghasem Sahab, and Mostafa Gandomi. "Multi expression programming: a new approach to formulation of soil classification." Engineering with Computers 26, no. 2 (2009): 111–18. http://dx.doi.org/10.1007/s00366-009-0140-7.

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Dissertations / Theses on the topic "Multi expression programming"

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Cattani, Philip Thomas. "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification." Thesis, University of Kent, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651.

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Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin's theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, name
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Masimula, Steven Mandla. "Gene expression programming for logic circuit design." 2017. http://hdl.handle.net/10500/23617.

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Finding an optimal solution for the logic circuit design problem is challenging and time-consuming especially for complex logic circuits. As the number of logic gates increases the task of designing optimal logic circuits extends beyond human capability. A number of evolutionary algorithms have been invented to tackle a range of optimisation problems, including logic circuit design. This dissertation explores two of these evolutionary algorithms i.e. Gene Expression Programming (GEP) and Multi Expression Programming (MEP) with the aim of integrating their strengths into a new Genetic Prog
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Huang, Bo-Jyun, and 黃柏鈞. "Using Multi-Gene Expression Programming in the Prediction Model for Customer Churn：The Case of Laundry SMEs." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/73177801405833036542.

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碩士<br>輔仁大學<br>資訊管理學系<br>100<br>In recent years, the core of the enterprise strategy is gradually transformed into customer-centric, and the customer churn issues are taken seriously. However, with the information technology booming, customer information and transaction data has been accumulated surprisingly. It is exactly a good opportunity for SMEs （Small and medium enterprise, SMEs）to improve the ability of its IT applications. Customer information and transaction data implied customer behavior patterns. In so doing, a customer churn warming model helps enterprises to develop service strateg
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Book chapters on the topic "Multi expression programming"

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Deng, Wei, Pei He, and Zhi Huang. "Multi-Expression Based Gene Expression Programming." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38466-0_49.

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Zhang, Qingke, Xiangxu Meng, Bo Yang, and Weiguo Liu. "MREP: Multi-Reference Expression Programming." In Intelligent Computing Theories and Application. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_3.

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Oltean, Mihai, and D. Dumitrescu. "Evolving TSP Heuristics Using Multi Expression Programming." In Computational Science - ICCS 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24687-9_99.

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Ávila, J. L., E. L. Gibaja, and S. Ventura. "Multi-label Classification with Gene Expression Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02319-4_76.

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Oltean, Mihai, and Crina Groşan. "Evolving Evolutionary Algorithms Using Multi Expression Programming." In Advances in Artificial Life. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39432-7_70.

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Deng, Wei, and Pei He. "Improving Multi Expression Programming Using Reuse-Based Evaluation." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9_33.

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Groşan, Crina, Ajith Abraham, and and Sang Yong Han. "MEPIDS: Multi-Expression Programming for Intrusion Detection System." In Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499305_17.

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Senthilnath, J., S. N. Omkar, V. Mani, Ashoka Vanjare, and P. G. Diwakar. "Multi-Temporal Satellite Image Analysis Using Gene Expression Programming." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1602-5_109.

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Liu, Ruochen, Jianxia Li, and Manman He. "Multi-objective Gene Expression Programming Based Automatic Clustering Method." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1648-7_13.

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Wen, Jiebin, Wenzhong Yang, LieJun Wang, Wenyu Wei, Sixiang Tan, and Yongzhi Wu. "Cross-database Micro Expression Recognition Based on Apex Frame Optical Flow and Multi-head Self-attention." In Parallel Architectures, Algorithms and Programming. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0010-4_12.

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Conference papers on the topic "Multi expression programming"

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Grosan, Crina, Ajith Abraham, Vitorino Ramos, and Sang Yong Han. "Stock Market Prediction Using Multi Expression Programming." In 2005 Purtuguese Conference on Artificial Intelligence. IEEE, 2005. http://dx.doi.org/10.1109/epia.2005.341268.

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Cattani, Phil T., and Colin G. Johnson. "ME-CGP: Multi Expression Cartesian Genetic Programming." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586478.

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Wang, Weihong, Wenrou Lin, and Qu Li. "Image retrieval based on Multi Expression Programming algorithms." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818191.

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Ma, Jun, Fenghui Gao, Shuangrong Liu, and Lin Wang. "Linear-dependent multi-interpretation neuro-encoded expression programming." In GECCO '21: Genetic and Evolutionary Computation Conference. ACM, 2021. http://dx.doi.org/10.1145/3449726.3459498.

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Reyes, Oscar, Jose María Moyano, José María Luna, and Sebastián Ventura. "A gene expression programming method for multi-target regression." In the International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3230905.3230910.

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Dehuri, Satchidananda, and Sung-Bae Cho. "Multi-objective Classification Rule Mining Using Gene Expression Programming." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.27.

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Yuehui Chen, Guangfeng Jia, and Liming Xiu. "Design of flexible neural trees using multi expression programming." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597554.

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Wu, Jiang, Changjie Tang, Taiyong Li, Shaojie Qiao, Yue Jiang, and Shangyu Ye. "Parallel Multi-objective Gene Expression Programming Based on Area Penalty." In 2008 International Conference on Computer Science and Information Technology (ICCSIT). IEEE, 2008. http://dx.doi.org/10.1109/iccsit.2008.140.

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Zhang, Qingke, Bo Yang, Lin Wang, and Fuxiang Zhu. "Predicting Cement Compressive Strength Using Double-Layer Multi-expression Programming." In 2012 Fourth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccis.2012.207.

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Dai, Shucheng, Changjie Tang, Mingfang Zhu, et al. "MERGE: A Novel Evolutionary Algorithm Based on Multi Expression Gene Programming." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.164.

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Reports on the topic "Multi expression programming"

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Carter, Becky. Inclusion in Crisis Response, Recovery and Resilience. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/k4d.2021.079.

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This rapid review provides examples of what has worked to include people in humanitarian assistance who experience heightened vulnerability during crises, due to social inequalities and discrimination relating to gender, age, disability, sexual orientation, gender identity and/or expression, and sex characteristics; and religious belief . Overall, robust evidence is limited for what are, in most cases, relatively new areas of practice in challenging crisis situations. However, the literature does identify promising practices. Emerging themes from the research on what has potential for improvin
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