http://www.bibsonomy.org/burst/concept/tag/expressionBibSonomy BuRST Feed for /concept/tag/expression2008-07-21T00:30:44+02:00http://www.bibsonomy.org/bibtex/246c32ff625d1492c2452da84dc370c04/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Programming, strategy, Evolutionary programming, regression, algorithms, symbolic linear Computation, genetic generation. Tic-Tac-Toe, representation, heuristics Expression Multi game <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> and D. <a href="http://www.bibsonomy.org/author/Dumitrescu">Dumitrescu</a> </span><em>May2002. </em><em>submitted . </em>Thu Jun 19 17:46:40 CEST 2008MaysubmittedMulti Expression Programming2002Programming, strategy, Evolutionary programming, regression, algorithms, symbolic linear Computation, genetic generation. Tic-Tac-Toe, representation, heuristics Expression Multi game 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.http://www.bibsonomy.org/bibtex/26e2c03fad647d9c7e71585baadb27f9e/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00circuits genetic expression digital multi algorithms, programming, <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> </span><em>7th Joint Conference on Information Sciences, </em><em>1, </em><em>page315--318. </em><em>North Carolina, </em><em>Association for Intelligent Machinery, </em><em>September2003. </em>Thu Jun 19 17:46:40 CEST 2008North Carolina7th Joint Conference on Information SciencesSeptember315--318Solving Even-Parity Problems using Multi Expression Programming12003circuits genetic expression digital multi algorithms, programming, 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.http://www.bibsonomy.org/bibtex/2e9845953e31683cfe8804154d5b3b49f/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00algorithms, programming multi expression programming, genetic <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> and D. <a href="http://www.bibsonomy.org/author/Dumitrescu">Dumitrescu</a> </span><em>Computational Science - ICCS 2004: 4th International Conference, Part II, </em><em>volume3037ofLecture Notes in Computer Science, </em><em>page670--673. </em><em>Krakow, Poland, </em><em>Springer-Verlag, </em><em>6-9 June2004. </em>Thu Jun 19 17:46:40 CEST 2008Krakow, PolandComputational Science - ICCS 2004: 4th International Conference, Part II6-9 June670--673Lecture Notes in Computer ScienceEvolving {TSP} Heuristics Using Multi Expression Programming30372004algorithms, programming multi expression programming, genetic 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.http://www.bibsonomy.org/bibtex/2abe23b0c1ab16eef70242361f52fbb58/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00multi genetic programming programming, algorithms, expression <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> and Crina <a href="http://www.bibsonomy.org/author/Grosan">Grosan</a> and Mihaela <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> </span><em>Computational Science - ICCS 2004: 4th International Conference, Part III, </em><em>volume3038ofLecture Notes in Computer Science, </em><em>page1257--1264. </em><em>Krakow, Poland, </em><em>Springer, </em><em>6-9 June2004. </em>Thu Jun 19 17:46:40 CEST 2008Krakow, PolandComputational Science - ICCS 2004: 4th International Conference, Part III6-9 June1257--1264Lecture Notes in Computer ScienceEvolving Digital Circuits for the Knapsack Problem30382004multi genetic programming programming, algorithms, expression 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.http://www.bibsonomy.org/bibtex/25756c465f3d74947215babb98adc39b2/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00multi digital expression circuits genetic programming, algorithms, <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> and Crina <a href="http://www.bibsonomy.org/author/Grosan">Grosan</a> </span><em>Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware, </em><em>page87--97. </em><em>Seattle, </em><em>IEEE Press, </em><em>24-26 June2004. </em>Thu Jun 19 17:46:40 CEST 2008SeattleProceedings of the 2004 NASA/DoD Conference on Evolvable Hardware24-26 June87--97Evolving Digital Circuits using Multi Expression Programming2004multi digital expression circuits genetic programming, algorithms, 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.http://www.bibsonomy.org/bibtex/2e4b7d66f069751daa8b0fa796ba86295/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00gene expression algorithms, genetic programming, programming <span style="color:#555555;">Mehmet <a href="http://www.bibsonomy.org/author/Saltan">Saltan</a> and Serdal <a href="http://www.bibsonomy.org/author/Terzi">Terzi</a> </span><em>Indian Journal of Engineering and Materials Sciences</em><em>12(1):42--50</em><em>February2005. </em>Thu Jun 19 17:46:40 CEST 2008Indian Journal of Engineering and Materials SciencesFebruary142--50Comparative analysis of using artificial neural networks ({ANN}) and gene expression programming ({GEP}) in backcalculation of pavement layer thickness122005gene expression algorithms, genetic programming, programming 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.http://www.bibsonomy.org/bibtex/228f3f42af93dff97a776fbbe0243cdb3/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00schemas, building-block size, blocks, population algorithms, expression partition, supply, genetic building programming, <span style="color:#555555;">Kumara <a href="http://www.bibsonomy.org/author/Sastry">Sastry</a> and Una-May <a href="http://www.bibsonomy.org/author/O&#039;Reilly">O'Reilly</a> and David E. <a href="http://www.bibsonomy.org/author/Goldberg">Goldberg</a> and David <a href="http://www.bibsonomy.org/author/Hill">Hill</a> </span><em>Genetic Programming Theory and Practice, </em><em>chapter 9, </em><em>Kluwer, </em>(<em>2003</em>)Thu Jun 19 17:46:40 CEST 2008Genetic Programming Theory and Practice9137--154Building Block Supply in Genetic Programming2003schemas, building-block size, blocks, population algorithms, expression partition, supply, genetic building programming, 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.http://www.bibsonomy.org/bibtex/268bfa0bc6e1d96ad3c70488119d68fc1/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00channel antagonists algorithms, QSAR, Gene Expression programming, Calcium genetic Programming, <span style="color:#555555;">Hong Zong <a href="http://www.bibsonomy.org/author/Si">Si</a> and Tao <a href="http://www.bibsonomy.org/author/Wang">Wang</a> and Ke Jun <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Zhi De <a href="http://www.bibsonomy.org/author/Hu">Hu</a> and Bo Tao <a href="http://www.bibsonomy.org/author/Fan">Fan</a> </span><em>Bioorganic \&amp; Medicinal Chemistry</em><em>14(14):4834--4841</em><em>15 July2006. </em>Thu Jun 19 17:46:40 CEST 2008Bioorganic \& Medicinal Chemistry15 July144834--4841{QSAR} study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming142006channel antagonists algorithms, QSAR, Gene Expression programming, Calcium genetic Programming, 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.http://www.bibsonomy.org/bibtex/204036602ea4576afb14ea00621380f9b/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00machine programming, genetic Support Programming, Expression vector Gene algorithms, <span style="color:#555555;">H. Z. <a href="http://www.bibsonomy.org/author/Si">Si</a> and K. J. <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Z. D. <a href="http://www.bibsonomy.org/author/Hu">Hu</a> and B. T. <a href="http://www.bibsonomy.org/author/Fan">Fan</a> </span><em>QSAR \&amp; Combinatorial Science</em><em>26(1):41--50</em><em>January2007. </em>Thu Jun 19 17:46:40 CEST 2008QSAR \& Combinatorial ScienceJanuary141--50{QSAR} Model for Prediction Capacity Factor of Molecular Imprinting Polymer Based on Gene Expression Programming262007machine programming, genetic Support Programming, Expression vector Gene algorithms, 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.http://www.bibsonomy.org/bibtex/2aa4d0ff8af507390517d3ec179cbebea/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic gene programming programming, expression algorithms, <span style="color:#555555;">James F. <a href="http://www.bibsonomy.org/author/{Smith, III}">Smith, III</a> </span><em>Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings, </em><em>volume3177ofLecture Notes in Computer Science, </em><em>page464--470. </em><em>Exeter, UK, </em><em>Springer, </em><em>August 25-272004. </em>Thu Jun 19 17:46:40 CEST 2008Exeter, UKIntelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, ProceedingsAugust 25-27464--470Lecture Notes in Computer ScienceGenetic Program Based Data Mining for Fuzzy Decision Trees31772004genetic gene programming programming, expression algorithms, 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 discussedhttp://www.bibsonomy.org/bibtex/286912c6feeb51dc1cc890d133faf8db7/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Expression instrumentation genetic Gene analysis analysis, gene expression energy algorithms, high computing, data algorithm, evolutionary programming, physics Programming, <span style="color:#555555;">Liliana <a href="http://www.bibsonomy.org/author/Teodorescu">Teodorescu</a> </span><em>IEEE Nuclear Science Symposium Conference Record, </em><em>1, </em><em>page143--147. </em><em>IEEE, </em><em>23-29 October2005. </em>Thu Jun 19 17:46:40 CEST 2008IEEE Nuclear Science Symposium Conference Record23-29 October143--147High energy physics data analysis with gene expression programming12005Expression instrumentation genetic Gene analysis analysis, gene expression energy algorithms, high computing, data algorithm, evolutionary programming, physics Programming, 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.http://www.bibsonomy.org/bibtex/24c5a80ced1840ab8db4c6430b4aa6566/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00algorithms, Programming, genetic Gene Event Expression programming, evolutionary algorithms selection, <span style="color:#555555;">Liliana <a href="http://www.bibsonomy.org/author/Teodorescu">Teodorescu</a> </span><em>IEEE Transactions on Nuclear Science</em><em>53(4 (part2)):2221--2227</em><em>August2006. </em>Thu Jun 19 17:46:40 CEST 2008IEEE Transactions on Nuclear ScienceAugust4 (part2)2221--2227Gene Expression Programming Approach to Event Selection in High Energy Physics532006algorithms, Programming, genetic Gene Event Expression programming, evolutionary algorithms selection, 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.http://www.bibsonomy.org/bibtex/26f2886d9da766736019a1ad91137e8ff/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Penmann algorithms, Method, Egirdir programming, gene expression genetic Lake <span style="color:#555555;">Ozlem <a href="http://www.bibsonomy.org/author/Terzi">Terzi</a> and M. <a href="http://www.bibsonomy.org/author/Erol Keskin">Erol Keskin</a> </span><em>Journal of Applied Sciences</em><em>5(3):508--512</em>(<em>2005</em>)Thu Jun 19 17:46:40 CEST 2008Journal of Applied Sciences3508--512Evaporation Estimation using Gene Expression Programming52005Penmann algorithms, Method, Egirdir programming, gene expression genetic Lake http://www.bibsonomy.org/bibtex/2998b1d85f05463519d5b0aa5d650cb39/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00pavements, gene Flexible highway programming, algorithms, genetic expression nondestructive testing <span style="color:#555555;">Serdal <a href="http://www.bibsonomy.org/author/Terzi">Terzi</a> </span><em>Journal of Applied Sciences</em><em>5(2):309--314</em>(<em>2005</em>)Thu Jun 19 17:46:40 CEST 2008Journal of Applied Sciences2309--314Modeling the Deflection Basin of Flexible Highway Pavements by Gene Expression Programming52005pavements, gene Flexible highway programming, algorithms, genetic expression nondestructive testing 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.http://www.bibsonomy.org/bibtex/2249fb037bd4ff2da0389f7f527f53741/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00algorithms, Gene Expression Programming, programming, genetic EHW <span style="color:#555555;">Xue song <a href="http://www.bibsonomy.org/author/Yan">Yan</a> and Wei <a href="http://www.bibsonomy.org/author/Wei">Wei</a> and Rui <a href="http://www.bibsonomy.org/author/Liu">Liu</a> and San you <a href="http://www.bibsonomy.org/author/Zeng">Zeng</a> and Lishan <a href="http://www.bibsonomy.org/author/Kang">Kang</a> </span><em>First NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2006),, </em><em>page194--199. </em><em>Istanbul, Turkey, </em><em>IEEE Computer Society, </em><em>15-18 June2006. </em>Thu Jun 19 17:46:40 CEST 2008Istanbul, TurkeyFirst {NASA}/{ESA} Conference on Adaptive Hardware and Systems ({AHS} 2006),15-18 June194--199Designing Electronic Circuits by Means of Gene Expression Programming2006algorithms, Gene Expression Programming, programming, genetic EHW 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.http://www.bibsonomy.org/bibtex/28b6862e6e662f0ea537da8a66f46c583/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic Gene expression algorithms, programming programming, <span style="color:#555555;">Kejun <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Yuxia <a href="http://www.bibsonomy.org/author/Hu">Hu</a> and Gang <a href="http://www.bibsonomy.org/author/Liu">Liu</a> </span><em>The Sixth World Congress on Intelligent Control and Automation, WCICA 2006, </em><em>1, </em><em>page3371--3375. </em><em>IEEE, </em><em>21-23 June2006. </em>Thu Jun 19 17:46:40 CEST 2008The Sixth World Congress on Intelligent Control and Automation, WCICA 200621-23 June3371--3375An Improved Gene Expression Programming for Solving Inverse Problem12006genetic Gene expression algorithms, programming programming, 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.http://www.bibsonomy.org/bibtex/20d01c5bfb43961426b94abbf3ffbf422/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00gene programming, genetic DE expression algorithms, <span style="color:#555555;">Qiongyun <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Chi <a href="http://www.bibsonomy.org/author/Zhou">Zhou</a> and Weimin <a href="http://www.bibsonomy.org/author/Xiao">Xiao</a> and Peter C. <a href="http://www.bibsonomy.org/author/Nelson">Nelson</a> and Xin <a href="http://www.bibsonomy.org/author/Li">Li</a> </span><em>Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006), </em><em>Seattle, WA, USA, </em><em>8-12 July2006. </em>Thu Jun 19 17:46:40 CEST 2008Seattle, WA, USALate breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}8-12 JulyUsing Differential Evolution for {GEP} Constant Creation2006gene programming, genetic DE expression algorithms, 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.http://www.bibsonomy.org/bibtex/27f7a2dc424e3ec0572027da0cb2ca450/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00algorithms, rule, data genetic mining, gene programming, classification expression GEP <span style="color:#555555;">Chi <a href="http://www.bibsonomy.org/author/Zhou">Zhou</a> and Weimin <a href="http://www.bibsonomy.org/author/Xiao">Xiao</a> and Thomas M. <a href="http://www.bibsonomy.org/author/Tirpak">Tirpak</a> and Peter C. <a href="http://www.bibsonomy.org/author/Nelson">Nelson</a> </span><em>IEEE Transactions on Evolutionary Computation</em><em>7(6):519--531</em><em>December2003. </em>Thu Jun 19 17:46:40 CEST 2008IEEE Transactions on Evolutionary ComputationDecember6519--531Evolving accurate and compact classification rules with gene expression programming72003algorithms, rule, data genetic mining, gene programming, classification expression GEP 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.http://www.bibsonomy.org/bibtex/2b42c90f4670ecfae1cd7bd2d1c33b424/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00approach, evolutionary system, immune algorithms, artificial programming, computation, cancer genetic gene expression classification, diagnosis wrapper <span style="color:#555555;">Shin <a href="http://www.bibsonomy.org/author/Ando">Ando</a> and Hitoshi <a href="http://www.bibsonomy.org/author/Iba">Iba</a> </span><em>Genetic Programming and Evolvable Machines</em><em>5(2):145--156</em><em>June2004. </em>Thu Jun 19 17:35:00 CEST 2008Genetic Programming and Evolvable MachinesJune2145--156Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning52004approach, evolutionary system, immune algorithms, artificial programming, computation, cancer genetic gene expression classification, diagnosis wrapper The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in Bioinformatics. The problem we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues. Two subtasks in such problem, feature subset selection and inductive learning has critical effect on each other. In the wrapper approach, combinatorial search of feature subset is done with performance of inductive learning as search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation is introduced, which uses Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals synergistic effect of genes in discrimination of the profiles.http://www.bibsonomy.org/bibtex/2eea2123852d217d1dead3c32c6ce34b7/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00programming, Gene Expression genetic Programming algorithms, <span style="color:#555555;">Ali <a href="http://www.bibsonomy.org/author/Aytek">Aytek</a> and M <a href="http://www.bibsonomy.org/author/Asce">Asce</a> and Murat <a href="http://www.bibsonomy.org/author/Alp">Alp</a> </span><em>Journal of Earth System Science</em><em>117(2):145--155</em><em>April2008. </em>Thu Jun 19 17:35:00 CEST 2008Journal of Earth System ScienceApril2145--155An application of artificial intelligence for rainfall-runoff modeling1172008programming, Gene Expression genetic Programming algorithms, This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.