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Querying Biological Sequences Docking Using Different Constraint Programming's: a Survey

B.Mallikarjuna Reddy, P Chandrasekhar, M.Ramakrishna Reddy
2015 International Journal of Computer Trends and Technology  
In mixed data extract the useful biological sequences data with subgroup discovery iterative genetic algorithm, Cluster based fuzzy genetic algorithm mining framework, hierarchical fuzzy rule based systems  ...  Generally Fuzzy set theory handling the issues related understandability patterns, insufficient and noisy data. Fuzzy set theory provides the faster solutions.  ...  Keywords: Fuzzy set theory, membership functions, fuzzy rules, KDD, learning techniques, genetic algorithm.  ... 
doi:10.14445/22312803/ijctt-v22p110 fatcat:ag36qn6m4jdkhlakxvrbbisyma

Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms

M.J. del Jesus, F. Hoffmann, L. JuncoNavascues, L. Sanchez
2004 IEEE transactions on fuzzy systems  
Index Terms-Boosting algorithms, evolutionary algorithms, fuzzy-rule-based classifiers, iterative learning.  ...  This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers.  ...  Adaboost Compared to Genetic Iterative Learning Fuzzy classifier induction with genetic iterative learning [5] operates in three stages.  ... 
doi:10.1109/tfuzz.2004.825972 fatcat:zgmi62zhdrftpetxxifjftnlsq

Multistep Fuzzy Classifier Design with Self-tuning Coevolutionary Algorithm

Roman B. Sergienko, Eugene Semenkin
2013 Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics  
This algorithm solves the problem of genetic algorithm parameters setting automatically.  ...  Results of numerical experiments for machine learning problems from UCI repository are presented.  ...  It allows refusing the genetic algorithm parameters setting without negative effect for algorithm efficiency. The second main result of our work is multistep fuzzy classifier design investigations.  ... 
doi:10.5220/0004426501130120 dblp:conf/icinco/SergienkoS13 fatcat:7f6nr5bbpfgznczjph6nkujk34

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

MJ. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero
2007 IEEE transactions on fuzzy systems  
This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in  ...  This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert.  ...  Hybrid Genetic Algorithm for the Induction of a Fuzzy Rule The hybrid GA extracts a single DNF fuzzy rule in an attempt to optimize the confidence and support.  ... 
doi:10.1109/tfuzz.2006.890662 fatcat:ftho6r54vvhgtkzhn5vcevicv4

Evolutionary Induction of Descriptive Rules in a Market Problem [chapter]

M.J. del Jesus, P. González, F. Herrera, M. Mesonero
2005 Studies in Computational Intelligence  
We study the use of Soft Computing methodologies, specifically Fuzzy Logic and Genetic Algorithms, in the design of the Data Mining algorithms most proper to this problem, descriptive induction algorithms  ...  Then we present an evolutionary model for the descriptive induction of fuzzy or crisp rules which describe subgroups.  ...  Iterative Model for the Extraction of Descriptive Fuzzy Rules The objective of the model for the extraction of descriptive fuzzy rules is to obtain a set of rules which give information on the majority  ... 
doi:10.1007/11004011_14 fatcat:cnts5mwjwvdbbly7f52u4xboq4

Selection of relevant features in a fuzzy genetic learning algorithm

A. Gonzalez, R. Perez
2001 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules.  ...  Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes.  ...  ACKNOWLEDGMENT The authors wish to thank NOAA for the provision of satellite pictures (both visible and EIR) for TC cases from 1990 to 1998.  ... 
doi:10.1109/3477.931534 pmid:18244806 fatcat:47rnx7vstjgg5huephylidvdqq

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  
Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows.  ...  In the presented application, genetic programming was deployed to evolve a fuzzy classifier and an example of real world application was presented.  ...  Fuzzy sets and fuzzy logic can be used for efficient data classification by fuzzy rules and fuzzy classifiers.  ... 
doi:10.1007/978-3-642-33227-2_28 fatcat:6ef3rneuczayla6f27z5c5tova

Evolutionary Neuro-Fuzzy Systems and Applications [chapter]

G. Castellano, C. Castiello, A. M. Fanelli, L. Jain
2007 Studies in Computational Intelligence  
The chapter also describes a particular approach that jointly uses neural learning and genetic optimisation to learn a fuzzy model from the given data and to optimise it for accuracy and interpretability  ...  For example, the use of a neural fuzzy system and an evolutionary fuzzy system hybridises the approximate reasoning mechanism of fuzzy systems with the learning capabilities of neural networks and evolutionary  ...  Structure and Parameter Learning Genetic algorithms may also be applied for structure optimisation of a fuzzy rule base.  ... 
doi:10.1007/978-3-540-72377-6_2 fatcat:mesecd4qrfasjkxbalcpmpsnoe

Learning cooperative linguistic fuzzy rules using the best-worst ant system algorithm

Jorge Casillas, Oscar Cordón, Iñaki Fernández de Viana, Francisco Herrera
2005 International Journal of Intelligent Systems  
In this article, we introduce a novel approach to the fuzzy rule learning problem with ant colony optimization (ACO) algorithms.  ...  Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the linguistic fuzzy rules from numerical data is an important task.  ...  fuzzy model performance. • The third one, proposed by Thrift, 32 is a classical fuzzy rule set learning method based on genetic algorithms. • Moreover, two COR-based methods based on a simulated annealing  ... 
doi:10.1002/int.20074 fatcat:ehp5iwij7bc4vngu4fhxks6vdm

Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems

H. Ishibuchi, T. Yamamoto, T. Nakashima
2005 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems.  ...  Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set.  ...  Some fuzzy genetics-based machine learning (GBML) algorithms have been proposed for designing fuzzy rule-based systems without linguistic knowledge from domain experts in the literature (see [3] for  ... 
doi:10.1109/tsmcb.2004.842257 pmid:15828663 fatcat:qjb34yh3ijdmfpiucohyr6ycdm

A Study on the Analysis of Genetic Algorithms with Various Classification Techniques for Feature Selection

E.P. Ephzibah, B. Sarojini, J.Emerald Sheela
2010 International Journal of Computer Applications  
Genetic algorithms are now days play a vital role among any other methodology in selecting the features based on the Theory of Evolution and on the "Survival of the fitness".  ...  Reducing the number of features pave way for various advantaged as well as simplifies the task.  ...  The inductive learning of a fuzzy rule-based classification system first of all determines a set of fuzzy rules from the set of instances and patterns.  ... 
doi:10.5120/1226-1784 fatcat:d6gk37do4vd45ih2ekbf4svemq

Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions

Hisao Ishibuchi
2007 IEEE International Fuzzy Systems conference proceedings  
Recently evolutionary multiobjective algorithms have been used for interpretability-accuracy tradeoff analysis of fuzzy systems.  ...  Evolutionary algorithms have been successfully used in many studies to design accurate and interpretable fuzzy systems under the name of genetic fuzzy systems.  ...  Thus multiple runs are required to generate a rule set in the iterative rule learning approach.  ... 
doi:10.1109/fuzzy.2007.4295487 dblp:conf/fuzzIEEE/Ishibuchi07 fatcat:wjbozagl6zevrcauiklvctmd7y

A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge

Mona Gamal, Ahmed Abo El-Fatoh, Shereef Barakat, Elsayed Radwan
2012 International Journal of Computer Applications  
Generating Fuzzy rules using Parallel Genetic Algorithms The process of generating fuzzy rules is responsible for designing the set of fuzzy rules of the knowledge based system.  ...  Parallel Genetic Algorithms Parallel genetic algorithms (PGAs) [23] [26] are parallel algorithms.  ... 
doi:10.5120/9696-4136 fatcat:6tl2ugfsrvahdpuukd2mzkfep4

Host based Anomaly Detection using Fuzzy Genetic Approach (FGA)

Harjinder Kaur, Nivit Gill
2013 International Journal of Computer Applications  
The paper proposes to detect anomalous user behavior on a single machine based on the system log files using fuzzy logic and genetic algorithms.  ...  A novel approach for design of fuzzy rules using genetic algorithm is given by S.V. Wong et al. [8] .  ...  In this paper, fuzzy logic and genetic algorithms have been used for host based anomaly detection.  ... 
doi:10.5120/13024-0026 fatcat:gfav4w23grfefh2t4avmuobhdq

Overview of the SLAVE learning algorithm: A review of its evolution and prospects

David García, Antonio González, Raúl Pérez
2014 International Journal of Computational Intelligence Systems  
SLAVE (Structural Learning Algorithm in a Vague Environment) was one of the first fuzzy-rule learning algorithms, and since its first implementation in 1994 it has been frequently used to benchmark new  ...  algorithms.  ...  In a first stage, we worked on feature construction for genetic learning algorithms based on the iterative learning rule approach.  ... 
doi:10.1080/18756891.2014.967008 fatcat:gzpe6pszabactnvknzdnsjy4vm
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