@article{ISI:000237399900049, title = {An internal antisense RNA regulates expression of the photosynthesis gene isiA}, author = {U. Duehring and Im Axmann and W. R. Hess and A. Wilde}, journal = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, number = 18, pages = {7054-7058}, volume = 103, year = 2006, issn = {0027-8424}, abstract = {Small regulatory noncoding RNAs exist in both eukaryotic and prokaryotic organisms. Most of these RNA transcripts are transencoded RNAs with short and only partial antisense complementarity to their target RNAs, which regulate gene expression by modifying mRNA stability and translation. In contrast, reports on the function of cis-encoded, perfectly complementary antisense RNAs in eubacteria are rare. Cyanobacteria respond to iron deficiency by expressing IsiA (iron stress-induced protein A), which forms a giant ring structure around photosystem I. Here, we show that this process is controlled by IsrR (iron stress-repressed RNA), a cis-encoded antisense RNA transcribed from the isiA noncoding strand. Artificial overexpression of IsrR under iron stress causes a strongly diminished number of IsiA-photosystem I supercomplexes, whereas IsrR depletion results in premature expression of IsiA. The coupled degradation of IsrR/isiA mRNA duplexes appears to be a reversible switch that can respond to environmental changes. IsrR is the only RNA known so far to regulate a photosynthesis component.}, biburl = {http://www.bibsonomy.org/bibtex/2a7fcf4b35f040ba8951cd0f2fed64713/microbio}, keywords = {expression light_harvesting regulation_of_gene iron_stress redox_stress cyanobacteria} } @article{ChiZhou:2003:TEC, title = {Evolving accurate and compact classification rules with gene expression programming}, author = {Chi Zhou and Weimin Xiao and Thomas M. Tirpak and Peter C. Nelson}, journal = {IEEE Transactions on Evolutionary Computation}, month = {December}, number = 6, pages = {519--531}, volume = 7, year = 2003, issn = {1089-778X}, size = {13 pages}, abstract = {Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for discovering classification rules by using gene expression programming (GEP), a new technique of genetic programming (GP) with linear representation. The antecedent of discovered rules may involve many different combinations of attributes. To guide the search process, we suggest a fitness function considering both the rule consistency gain and completeness. A multiclass classification problem is formulated as multiple two-class problems by using the one-against-all learning method. The covering strategy is applied to learn multiple rules if applicable for each class. Compact rule sets are subsequently evolved using a two-phase pruning method based on the minimum description length (MDL) principle and the integration theory. Our approach is also noise tolerant and able to deal with both numeric and nominal attributes. Experiments with several benchmark data sets have shown up to 20% improvement in validation accuracy, compared with C4.5 algorithms. Furthermore, the proposed GEP approach is more efficient and tends to generate shorter solutions compared with canonical tree-based GP classifiers.}, biburl = {http://www.bibsonomy.org/bibtex/27f7a2dc424e3ec0572027da0cb2ca450/brazovayeye}, keywords = {programming, data expression classification algorithms, mining, genetic GEP gene rule,} } @inproceedings{Zhang:gecco06lbp, title = {Using Differential Evolution for {GEP} Constant Creation}, address = {Seattle, WA, USA}, author = {Qiongyun Zhang and Chi Zhou and Weimin Xiao and Peter C. Nelson and Xin Li}, booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}}, editor = {J{\"{o}}rn Grahl}, month = {8-12 July}, year = 2006, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp130.pdf}, notes = {Distributed on CD-ROM at GECCO-2006}, abstract = {Gene Expression Programming (GEP) is a new evolutionary algorithm that incorporates both the idea of simple, linear chromosomes of fixed length used in Genetic Algorithms (GAs) and the structure of different sizes and shapes used in Genetic Programming (GP). As with other genetic programming algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this paper, we describe a new approach of constant generation using Differential Evolution (DE), which is a simple real-valued GA that has proven to be robust and efficient on parameter optimisation problems. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variants.}, biburl = {http://www.bibsonomy.org/bibtex/20d01c5bfb43961426b94abbf3ffbf422/brazovayeye}, keywords = {algorithms, programming, genetic expression gene DE} } @inproceedings{Zhang:2006:WCICA, title = {An Improved Gene Expression Programming for Solving Inverse Problem}, author = {Kejun Zhang and Yuxia Hu and Gang Liu}, booktitle = {The Sixth World Congress on Intelligent Control and Automation, WCICA 2006}, month = {21-23 June}, pages = {3371--3375}, publisher = {IEEE}, volume = 1, year = 2006, isbn = {1-4244-0332-4}, doi = {doi:10.1109/WCICA.2006.1712993}, abstract = {The basic principle of Gene expression programming (GEP) is introduced in this paper. An improved GEP algorithm called IGEP based on dynamic mutation operator which dealing with the inverse problem of parameter identification of complex function is presented, the algorithm complexity of the IGEP was given in the paper, furthermore, many simulation results show that the models set up by the paper are better than the models set up by classic GEP. A future study will consider the effects of applying IGEP to the inverse problem which sensitive to the time period.}, biburl = {http://www.bibsonomy.org/bibtex/28b6862e6e662f0ea537da8a66f46c583/brazovayeye}, keywords = {expression Gene programming algorithms, programming, genetic} } @inproceedings{conf/ahs/YanWLZK06, title = {Designing Electronic Circuits by Means of Gene Expression Programming}, address = {Istanbul, Turkey}, author = {Xue song Yan and Wei Wei and Rui Liu and San you Zeng and Lishan Kang}, booktitle = {First {NASA}/{ESA} Conference on Adaptive Hardware and Systems ({AHS} 2006),}, editor = {Adrian Stoica and Tughrul Arslan and Martin Suess and Senay Yal{\c c}in and Didier Keymeulen and Tetsuya Higuchi and Ricardo Salem Zebulum and Nizamettin Aydin}, month = {15-18 June}, pages = {194--199}, publisher = {IEEE Computer Society}, year = 2006, url = {http://doi.ieeecomputersociety.org/10.1109/AHS.2006.31}, bibdate = {2007-02-12}, isbn = {0-7695-2614-4}, abstract = {This work investigates the application of Gene Expression Programming(GEP) in the field of evolutionary electronics. GEP is a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators. We propose the new means for designing electronic circuits and introduces the encoding of the circuit as a chromosome, the genetic operators and the fitness function. For the case studies this means has proved to be efficient, experiments show that we have better results.}, biburl = {http://www.bibsonomy.org/bibtex/2249fb037bd4ff2da0389f7f527f53741/brazovayeye}, keywords = {genetic programming, EHW Expression Programming, algorithms, Gene} } @article{Terzi:2005:JAS, title = {Modeling the Deflection Basin of Flexible Highway Pavements by Gene Expression Programming}, author = {Serdal Terzi}, journal = {Journal of Applied Sciences}, number = 2, pages = {309--314}, volume = 5, year = 2005, url = {http://www.ansinet.org/fulltext/jas/jas52309-314.pdf}, issn = {1812-5654}, abstract = {Gene Expression Programming (GEP) is used in modelling the deflection basins measured on the surface of the flexible pavements. Back calculation of the pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using back calculation analysis, in situ material properties can be back calculated from the measured field data through appropriate analysis techniques. In order to back calculate reliable moduli, deflection basin must be realistically modelled. In this study, GEP was used to model the deflection basin characteristics. Experimental deflection data groups from NDT are used to show the capability of the GEP approach in modelling the deflection bowl. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function about the solution.}, biburl = {http://www.bibsonomy.org/bibtex/2998b1d85f05463519d5b0aa5d650cb39/brazovayeye}, keywords = {nondestructive expression algorithms, gene highway Flexible testing pavements, genetic programming,} } @article{OzlemTerzi:2005:JAS, title = {Evaporation Estimation using Gene Expression Programming}, author = {Ozlem Terzi and M. Erol Keskin}, journal = {Journal of Applied Sciences}, number = 3, pages = {508--512}, volume = 5, year = 2005, url = {http://www.ansinet.org/fulltext/jas/jas53508-512.pdf}, issn = {1812-5654}, biburl = {http://www.bibsonomy.org/bibtex/26f2886d9da766736019a1ad91137e8ff/brazovayeye}, keywords = {gene programming, genetic Lake Method, Egirdir Penmann expression algorithms,} } @article{Teodorescu:2006:IEEETNS, title = {Gene Expression Programming Approach to Event Selection in High Energy Physics}, author = {Liliana Teodorescu}, journal = {IEEE Transactions on Nuclear Science}, month = {August}, number = {4 (part2)}, pages = {2221--2227}, volume = 53, year = 2006, issn = {0018-9499}, doi = {doi:10.1109/TNS.2006.878571}, size = {7 pages}, abstract = {Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic Algorithms and Genetic Programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. It allowed automatic identification of selection rules that can be interpreted as cuts applied on the input variables. The signal/background classification accuracy was over 90percent in all cases.}, biburl = {http://www.bibsonomy.org/bibtex/24c5a80ced1840ab8db4c6430b4aa6566/brazovayeye}, keywords = {selection, algorithms evolutionary genetic Event Programming, programming, Expression algorithms, Gene} } @inproceedings{Teodorescu:2005:IEEnsscr, title = {High energy physics data analysis with gene expression programming}, author = {Liliana Teodorescu}, booktitle = {IEEE Nuclear Science Symposium Conference Record}, month = {23-29 October}, pages = {143--147}, publisher = {IEEE}, volume = 1, year = 2005, notes = {ISSN: 1082-3654 INSPEC Accession Number:8976991}, doi = {doi:10.1109/NSSMIC.2005.1596225}, abstract = {Gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic algorithms and genetic programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. The signal/background classification accuracy was over 90percent in all cases.}, biburl = {http://www.bibsonomy.org/bibtex/286912c6feeb51dc1cc890d133faf8db7/brazovayeye}, keywords = {Expression expression analysis, Gene analysis high data Programming, algorithm, genetic computing, instrumentation algorithms, evolutionary programming, gene physics energy} } @inproceedings{DBLP:conf/ideal/Smith04, title = {Genetic Program Based Data Mining for Fuzzy Decision Trees}, address = {Exeter, UK}, author = {James F. {Smith, III}}, booktitle = {Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings}, editor = {Zheng Rong Yang and Richard M. Everson and Hujun Yin}, month = {August 25-27}, pages = {464--470}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 3177, year = 2004, url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3177&spage=464}, bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {3-540-22881-0}, organisation = {IEEE}, doi = {doi:10.1007/b99975}, abstract = {A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program is discussed. A genetic program is an algorithm that evolves other algorithms or mathematical expressions. Methods of accelerating convergence of the data mining procedure including a new innovation based on computer algebra are examined. Experimental results related to using computer algebra are given. A comparison between a tree obtained using a genetic program and one constructed solely by interviewing experts is made. A genetic program evolved tree is shown to be superior to one created by hand using expertise alone. Finally, additional methods that have been used to validate the data mining algorithm are discussed}, biburl = {http://www.bibsonomy.org/bibtex/2aa4d0ff8af507390517d3ec179cbebea/brazovayeye}, keywords = {gene genetic programming expression algorithms, programming,} } @article{Si:2007:QCS, title = {{QSAR} Model for Prediction Capacity Factor of Molecular Imprinting Polymer Based on Gene Expression Programming}, author = {H. Z. Si and K. J. Zhang and Z. D. Hu and B. T. Fan}, journal = {QSAR \& Combinatorial Science}, month = {January}, number = 1, pages = {41--50}, volume = 26, year = 2007, doi = {doi:10.1002/qsar.200530187}, size = {10 pages}, abstract = {The Gene Expression Programming (GEP), as a novel type of learning machine, for the first time, has been in this study used to develop a quantitative structure-activity relationship model of 39 compounds of molecular imprinting polymer based on calculated chemical parameters. The comparison with heuristic method and support vector machines approaches reveals a good prediction of GEP.}, biburl = {http://www.bibsonomy.org/bibtex/204036602ea4576afb14ea00621380f9b/brazovayeye}, keywords = {Support vector machine Programming, programming, genetic Expression Gene algorithms,} } @article{Si:2006:BMC, title = {{QSAR} study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming}, author = {Hong Zong Si and Tao Wang and Ke Jun Zhang and Zhi De Hu and Bo Tao Fan}, journal = {Bioorganic \& Medicinal Chemistry}, month = {15 July}, number = 14, pages = {4834--4841}, volume = 14, year = 2006, doi = {doi:10.1016/j.bmc.2006.03.019}, abstract = {The gene expression programming, a novel machine learning algorithm, is used to develop quantitative model as a potential screening mechanism for a series of 1,4-dihydropyridine calcium channel antagonists for the first time. The heuristic method was used to search the descriptor space and select the descriptors responsible for activity. A nonlinear, six-descriptor model based on gene expression programming with mean-square errors 0.19 was set up with a predicted correlation coefficient (R2) 0.92. This paper provides a new and effective method for drug design and screening. Graphical abstract The log (1/IC50) for 45 1,4-dihydropyridines was modelled using the descriptors calculated from the molecular structure along with a quantitative structure\u2013activity relationship (QSAR) technique. The heuristic method (HM) and gene expression programming (GEP) were used to construct the linear and nonlinear prediction models, leading to a good prediction.}, biburl = {http://www.bibsonomy.org/bibtex/268bfa0bc6e1d96ad3c70488119d68fc1/brazovayeye}, keywords = {QSAR, programming, Gene Programming, antagonists Calcium Expression genetic channel algorithms,} } @incollection{sastry:2003:GPTP, title = {Building Block Supply in Genetic Programming}, author = {Kumara Sastry and Una-May O'Reilly and David E. Goldberg and David Hill}, booktitle = {Genetic Programming Theory and Practice}, chapter = 9, editor = {Rick L. Riolo and Bill Worzel}, pages = {137--154}, publisher = {Kluwer}, year = 2003, url = {http://www-illigal.ge.uiuc.edu/kumara/wp-content/files/2003012.pdf}, notes = {2003012.pdf refers to IlliGAL report April 2003}, size = {pages}, abstract = {We analyse building block supply in the initial population for genetic programming. Facetwise models for the supply of a single schema as well as for the supply of all schemas in a partition are developed. An estimate for the population size, given the size (or size distribution) of trees, that ensures the presence of all raw building blocks with a given error is derived using these facetwise models. The facetwise models and the population sizing estimate are verified with empirical results.}, biburl = {http://www.bibsonomy.org/bibtex/228f3f42af93dff97a776fbbe0243cdb3/brazovayeye}, keywords = {size, expression genetic schemas, blocks, population programming, building building-block supply, partition, algorithms,} } @article{saltan:2005:IJEMS, title = {Comparative analysis of using artificial neural networks ({ANN}) and gene expression programming ({GEP}) in backcalculation of pavement layer thickness}, author = {Mehmet Saltan and Serdal Terzi}, journal = {Indian Journal of Engineering and Materials Sciences}, month = {February}, number = 1, pages = {42--50}, volume = 12, year = 2005, url = {http://tef.sdu.edu.tr/~sterzi/GEP&ANN.pdf}, issn = {0971-4588}, abstract = {Pavement deflection data are often used to evaluate a pavement's structural condition non-destructively. It is essential not only to evaluate the structural integrity of an existing pavement but also to have accurate information on pavement surface condition in order to establish a reasonable pavement rehabilitation design system. Pavement layers are characterised by their elastic moduli estimated from surface deflections through back calculation. Backcalculating the pavement layer moduli is a well-accepted procedure for the evaluation of the structural capacity of pavements. The ultimate aim of the back calculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, flexible pavement layer thicknesses together with in-situ material properties can be back calculated from the measured field data through appropriate analysis techniques. In this study, artificial neural networks (ANN) and gene expression programming (GEP) are used in back calculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the ANN and GEP approaches in back calculating the pavement layer thickness and compared each other. These approaches can be easily and realistically performed to solve the optimisation problems which do not have a formulation or function about the solution.}, biburl = {http://www.bibsonomy.org/bibtex/2e4b7d66f069751daa8b0fa796ba86295/brazovayeye}, keywords = {algorithms, genetic programming expression gene programming,} } @inproceedings{oltean:2004:EH, title = {Evolving Digital Circuits using Multi Expression Programming}, address = {Seattle}, author = {Mihai Oltean and Crina Grosan}, booktitle = {Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware}, editor = {Ricardo S. Zebulum and David Gwaltney and Gregory Horbny and Didier Keymeulen and Jason Lohn and Adrian Stoica}, month = {24-26 June}, pages = {87--97}, publisher = {IEEE Press}, year = 2004, url = {http://www.cs.ubbcluj.ro/~moltean/oltean_eh04.pdf}, email = {moltean@cs.ubbcluj.ro}, doi = {doi:10.1109/EH.2004.1310814}, size = {8 pages}, abstract = {Multi Expression Programming (MEP) is a Genetic Programming (GP) variant that uses linear chromosomes for solution encoding. A unique MEP feature is its ability of encoding multiple solutions of a problem in a single chromosome. These solutions are handled in the same time complexity as other techniques that encode a single solution in a chromosome. In this paper MEP is used for evolving digital circuits. MEP is compared to Cartesian Genetic Programming (CGP) a technique widely used for evolving digital circuits by using several well-known problems in the field of electronic circuit design. Numerical experiments show that MEP outperforms CGP for the considered test problems.}, biburl = {http://www.bibsonomy.org/bibtex/25756c465f3d74947215babb98adc39b2/brazovayeye}, keywords = {algorithms, programming, expression genetic digital multi circuits} } @inproceedings{oltean:2004:ICCS, title = {Evolving Digital Circuits for the Knapsack Problem}, address = {Krakow, Poland}, author = {Mihai Oltean and Crina Grosan and Mihaela Oltean}, booktitle = {Computational Science - ICCS 2004: 4th International Conference, Part III}, editor = {Marian Bubak and Geert Dick {van Albada} and Peter M. A. Sloot and Jack Dongarra}, month = {6-9 June}, pages = {1257--1264}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 3038, year = 2004, url = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3038&spage=1257}, email = {moltean@cs.ubbcluj.ro}, isbn = {3-540-22116-6}, doi = {doi:10.1007/b97989}, size = {8 pages}, abstract = {Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding. A unique feature of MEP is its ability of encoding multiple solutions of a problem in a single chromosome. In this paper we use Multi Expression Programming for evolving digital circuits for a well-known NP-Complete problem: the knapsack (subset sum) problem. Numerical experiments show that Multi Expression Programming performs well on the considered test problems.}, biburl = {http://www.bibsonomy.org/bibtex/2abe23b0c1ab16eef70242361f52fbb58/brazovayeye}, keywords = {multi genetic programming expression programming, algorithms,} } @inproceedings{oltean:2004:ICCS_TSP, title = {Evolving {TSP} Heuristics Using Multi Expression Programming}, address = {Krakow, Poland}, author = {Mihai Oltean and D. Dumitrescu}, booktitle = {Computational Science - ICCS 2004: 4th International Conference, Part II}, editor = {Marian Bubak and Geert Dick {van Albada} and Peter M. A. Sloot and Jack Dongarra}, month = {6-9 June}, pages = {670--673}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science}, volume = 3037, year = 2004, url = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3037&spage=670}, bibsource = {DBLP, http://dblp.uni-trier.de}, email = {moltean@cs.ubbcluj.ro}, isbn = {3-540-22115-8}, doi = {doi:10.1007/b97988}, size = {4 pages}, abstract = {Multi Expression Programming (MEP) is used for evolving a Travelling Salesman Problem (TSP) heuristic for graphs satisfying triangle inequality. Evolved MEP heuristic is compared with Nearest Neighbour Heuristic (NN) and Minimum Spanning Tree Heuristic (MST) on some difficult problems in TSPLIB. The results emphasises that evolved MEP heuristic is better than the compared algorithm for the considered test problems.}, biburl = {http://www.bibsonomy.org/bibtex/2e9845953e31683cfe8804154d5b3b49f/brazovayeye}, keywords = {programming algorithms, programming, genetic expression multi} } @inproceedings{Oltean:2003:JCIS2, title = {Solving Even-Parity Problems using Multi Expression Programming}, address = {North Carolina}, author = {Mihai Oltean}, booktitle = {7th Joint Conference on Information Sciences}, editor = {Ken Chen (et al)}, month = {September}, pages = {315--318}, publisher = {Association for Intelligent Machinery}, volume = 1, year = 2003, url = {http://www.mep.cs.ubbcluj.ro/oltean_fea2003_2.pdf}, email = {moltean@cs.ubbcluj.ro}, size = {4 pages}, abstract = {Multi Expression Programming (MEP) is used for solving even-parity problems. Numerical experiments show that MEP outperforms Genetic Programming (GP) with more than one order of magnitude for the considered test cases.}, biburl = {http://www.bibsonomy.org/bibtex/26e2c03fad647d9c7e71585baadb27f9e/brazovayeye}, keywords = {digital circuits programming, genetic multi algorithms, expression} } @misc{oltean:2002:MEP, title = {Multi Expression Programming}, author = {Mihai Oltean and D. Dumitrescu}, month = {May}, note = {submitted}, year = 2002, url = {http://www.mep.cs.ubbcluj.ro/oltean_pdf.pdf}, email = {ddumitr@nessie.cs.ubbcluj.ro}, notes = {Note critisism on GP-list of {"}, size = {33 pages}, abstract = {In this paper a new evolutionary paradigm, called Multi-Expression Programming (MEP), intended for solving computationally difficult problems is proposed. A new encoding method is designed. MEP individuals are linear entities that encode complex computer programs. In this paper MEP is used for solving some computationally difficult problems like symbolic regression, game strategy discovering, and for generating heuristics. Other exciting applications of MEP are suggested. Some of them are currently under development. MEP is compared with Gene Expression Programming (GEP) by using a well-known test problem. For the considered problems MEP performs better than GEP.}, biburl = {http://www.bibsonomy.org/bibtex/246c32ff625d1492c2452da84dc370c04/brazovayeye}, keywords = {generation. Programming, linear Tic-Tac-Toe, Expression Evolutionary genetic heuristics representation, Multi regression, symbolic strategy, algorithms, game Computation, programming,} }