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Learning to rank using evolutionary computation

Shuaiqiang Wang, Jun Ma, Jiming Liu
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
Nowadays ranking function discovery approaches using Evolutionary Computation (EC), especially Genetic Programming (GP), have become an important branch in the Learning to Rank for Information Retrieval  ...  Besides, according to the introduced framework, we propose RankIP, a ranking function discovery approach using Immune Programming (IP).  ...  Therefore, it would be very beneficial to the use of EC in the IR field, especially for the learning to Permission to make digital or hard copies of all or part of this work for personal or classroom use  ... 
doi:10.1145/1645953.1646254 dblp:conf/cikm/WangML09 fatcat:23wvzm4tibbojbpmmvvwf2wsam

Evolutionary Computation and Genetic Programming [chapter]

Wolfgang Banzhaf
2013 Engineered Biomimicry  
We discuss Evolutionary Computation, in particular Genetic Programming, as examples of drawing inspiration from biological systems.  ...  We set the choice of evolution as a source for inspiration in context, discuss the history of Evolutionary Computation and its variants before looking more closely at Genetic Programming.  ...  After a general discussion of algorithms derived from evolution (Evolutionary Algorithms or Evolutionary Computing), we consider in more detail the most modern branch of this area, Genetic Programming.  ... 
doi:10.1016/b978-0-12-415995-2.00017-9 fatcat:76wbwskdozhq5cwdxcf6zlhqni

One-Class Genetic Programming [chapter]

Robert Curry, Malcolm I. Heywood
2009 Lecture Notes in Computer Science  
Finally, the architecture makes extensive use of active learning to reinforce the scalability of the overall approach.  ...  A multi-objective fitness function in combination with a local membership function is then used to encourage a co-operative coevolutionary decomposition of the original problem under a novelty detection  ...  The Pareto ranking determines the tournament winners, or parents, from which genetic operators can be applied to create children, Step 14.  ... 
doi:10.1007/978-3-642-01181-8_1 fatcat:kpextp7y3fhqzjh2hu3hgtdvgm

Controlled Gene-Accumulation Programming

Najla Al-Saati, Nidhal Al-Assady
2009 ˜Al-œRafidain journal for computer sciences and mathematics  
Chromosome flip folding is a new crossover operator introduced in this work; it will prove to be efficient in introducing new genetic material.  ...  It has a much faster execution time compared to the well known Gene Expression Programming method.  ...  Introduction Controlled Gene-Accumulation Programming (CGAP) [1] , is a new genotype/ phenotype system that uses the idea of evolution to generate computer programs to solve real-world problems.  ... 
doi:10.33899/csmj.2009.163766 fatcat:7do25r3ni5fzjk3o4iz6wlk2ee

Data Mining by Symbolic Fuzzy Classifiers and Genetic Programming [chapter]

Suhail Owais, Pavel Krömer, Jan Platoš, Václav Snášel, Ivan Zelinka
2013 Advances in Intelligent Systems and Computing  
In the presented application, genetic programming was deployed to evolve a fuzzy classifier and an example of real world application was presented.  ...  Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows.  ...  Another interesting investigation using symbolic regression was carried out by [26] working on Artificial Immune Systems or/and systems, which are not using tree structures like linear genetic programming  ... 
doi:10.1007/978-3-642-33227-2_28 fatcat:6ef3rneuczayla6f27z5c5tova

Attaining Human–Competitive Game Playing with Genetic Programming [chapter]

M. Sipper
2006 Lecture Notes in Computer Science  
This paper has a twofold objective: first, to review our recent results of applying genetic programming in the domain of games; second, to formulate the merits of genetic programming in acting as a tool  ...  We have recently shown that genetically programming game players, after having imbued the evolutionary process with human intelligence, produces human-competitive strategies for three games: backgammon  ...  ACKNOWLEDGEMENTS We are grateful to Assaf Zaritsky, John Koza, and the anonymous reviewers for their many helpful comments.  ... 
doi:10.1007/11861201_4 fatcat:csudpvnc5fhnngyyd7a2pzolhe

An Algorithm Evaluation for Discovering Classification Rules with Gene Expression Programming

Alain Guerrero-Enamorado, Carlos Morell, Amin Y. Noaman, Sebastián Ventura
2016 International Journal of Computational Intelligence Systems  
In this paper, a new algorithm for classification using gene expression programming is proposed to accomplish this task, which was compared with several classical state-ofthe-art rule-based classifiers  ...  In recent years, evolutionary algorithms have been used for classification tasks.  ...  assess the behavior of evolutionary learning and Soft Computing based techniques.  ... 
doi:10.1080/18756891.2016.1150000 fatcat:zgbgymfvjbcgfbhgosiowlzwi4

Interday and Intraday Stock Trading Using Probabilistic Adaptive Mapping Developmental Genetic Programming and Linear Genetic Programming [chapter]

Garnett Wilson, Wolfgang Banzhaf
2010 Studies in Computational Intelligence  
A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks in the technology sector.  ...  Both interday and intraday data for these stocks were analyzed, where both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit  ...  Stock Analysis Using Developmental and Linear GP Genetic programing is one of a family of algorithms in machine learning classified as evolutionary methods.  ... 
doi:10.1007/978-3-642-13950-5_11 fatcat:npwnytv77vfezjp5mbxx2d6pee

Genetic Programming III - Darwinian Invention and Problem Solving

Peter Nordin
1999 Evolutionary Computation  
Special thanks goes to our reviewers, who stuck with us through a major reorganization of the book and provided insightful and useful comments.  ...  Acknowledgments We would like to acknowledge the help of our editor, Denise Penrose, and that of Edward Wade and Emilia Thiuri, at Morgan Kaufmann Publishers.  ...  Recall that genetic programming is a method for evolving computer programs, using evolutionary operators.  ... 
doi:10.1162/evco.1999.7.4.451 fatcat:27732t6mfvgt7mzpbvkrv6wcqm

Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

Jen-Yuan Yeh, Jung-Yi Lin
2017 Malaysian Journal of Computer Science  
Keywords: learning to rank for information retrieval, ranking function, supervised learning, layered multipopulation genetic programming, LAGEP, LETOR Traditional IR models, including the Boolean model  ...  A straightforward way of efficiently finding a solution by directly optimizing evaluation measures is to use genetic programming (GP).  ...  [51] presents RankIP, a ranking function discovery method using immune programming for its high diversity. RankDE [4] is the first LTR method that uses differential evolution.  ... 
doi:10.22452/mjcs.vol30no1.3 fatcat:y2boc575xjhu7nkw5eutgpqioi

Simultaneous generation of prototypes and features through genetic programming

Mauricio Garcia-Limon, Hugo Jair Escalante, Eduardo Morales, Alicia Morales-Reyes
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
This paper introduces a genetic programming approach to tackle the simultaneous generation of prototypes and features to be used for classification with a NN classifier.  ...  The proposed method learns to combine instances and attributes to produce a set of prototypes and a new feature space for each class of the classification problem via genetic programming.  ...  SGPFGP learns to combine instances and attributes to generate class-specific prototypes and features via genetic programming.  ... 
doi:10.1145/2576768.2598356 dblp:conf/gecco/Garcia-LimonEMM14 fatcat:rzjwhq6edzd2rj2lovupuahj2q

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Varun Kumar Ojha, Ajith Abraham, Václav Snášel
2017 Applied Soft Computing  
We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model.  ...  The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features.  ...  Similarly, in [34] , authors proposed to apply multi-expression programming (MEP) [35] for FNT structure optimization and immune programming algorithm [36] for the parameter vector optimization.  ... 
doi:10.1016/j.asoc.2016.09.035 fatcat:y23x4opvifcareeddzmtqkfcfa

Towards identifying salient patterns in genetic programming individuals

András Joó, Juan Pablo Neirotti
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
This thesis addresses the problem of offline identification of salient patterns in genetic programming individuals.  ...  (a) they are representative for the evolutionary run and/or search space, (b) they are human-friendly and (c) their numbers are within reasonable limits.  ...  Acknowledgments I would like to thank Juan P. Neirotti for his full support during these years.  ... 
doi:10.1145/1569901.1570217 dblp:conf/gecco/JooN09 fatcat:j4hjmnmeffgg3mrnmqxshuz5uq

Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming [chapter]

Garnett Wilson, Wolfgang Banzhaf
2009 Lecture Notes in Computer Science  
A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors.  ...  PAM DGP outperformed, or was competitive with, LGP for all stocks tested.  ...  In recent years, a number of Evolutionary Computation-inspired algorithms, including genetic programming (GP), have been applied to the analysis of financial markets with a reassuring degree of success  ... 
doi:10.1007/978-3-642-01129-0_21 fatcat:5gtleoxgfnfvlmvagqqyqh5cga

Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms

Gang Chen, Chor Ping Low, Zhonghua Yang
2009 IEEE Transactions on Evolutionary Computation  
Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs).  ...  Meanwhile, two solution exchange rules R4 and R5 have been introduced to further exploit the preserved genetic diversity.  ...  CONCLUSION This paper has been aimed at preserving and exploiting genetic diversity in evolutionary programming (EP) algorithms.  ... 
doi:10.1109/tevc.2008.2011742 fatcat:utzr4ptt25ex7praq6gkeu76jm
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