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Classifier systems and genetic algorithms

L.B. Booker, D.E. Goldberg, J.H. Holland
1989 Artificial Intelligence  
Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm).  ...  possible extensions of classifier systems.  ...  work on classifier systems and genetic algorithms may be found in the books Genetic Algorithms and Simulated Annealing (Davis [10] ) and Genetic Algorithms and Their Applications (Grefenstette [20]  ... 
doi:10.1016/0004-3702(89)90050-7 fatcat:5rwumg7iwrbglmhr5mv6s7vfcq


Simon Günter, Horst Bunke
2009 Series in Machine Perception and Artificial Intelligence  
In this paper we describe a weighted voting scheme where the weights are obtained by a genetic algorithm.  ...  However, due to a great variety of individual writing styles, the problem is very difficult and far from being solved.  ...  Urs-Victor Marti for providing the handwritten word recognizer and Matthias Zimmermann for the segmentation of a part of the IAM database.  ... 
doi:10.1142/9789812834461_0005 dblp:series/smpai/GunterB09 fatcat:obwgjy2siveuha25kbtfjxux3i

Brain Tumor Segmentation in Magnetic Resonance Images using Genetic Algorithm Clustering and AdaBoost Classifier

Gustavo C. Oliveira, Renato Varoto, Alberto Cliquet Jr.
2018 Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies  
We present a technique for automatic brain tumor segmentation in magnetic resonance images, combining a modified version of a Genetic Algorithm Clustering method with an AdaBoost Classifier.  ...  Our implementation of the Genetic Algorithm Clustering method presents improvements compared to the original method.  ...  ACKNOWLEDGEMENTS The authors would like to thank the support by grants from São Paulo Research Foundation (FAPESP), Brazilian Federal Agency for Support and Evaluation of Graduate Education (Capes) and  ... 
doi:10.5220/0006534900770082 dblp:conf/biostec/OliveiraVC18 fatcat:wow36ugl7bgbrf3fxuakl6bxwy

Causality-based Attribute Weighting via Information Flow and Genetic Algorithm for Naive Bayes Classifier

Ming Li, Kefeng Liu
2019 IEEE Access  
In this paper, we propose a novel causality-based attribute weighting method to establish the weighted NBC called IFG-WNBC, where causal information flow (IF) theory and genetic algorithm (GA) are adopted  ...  Naive Bayes classifier (NBC) is an effective classification technique in data mining and machine learning, which is based on the attribute conditional independence assumption.  ...  We first discuss the weighted Naive Bayes classifier (NBC) and explain the causal information flow (IF) theory. We also describe the genetic algorithm (GA). A.  ... 
doi:10.1109/access.2019.2947568 fatcat:w5gcuh4r4vgbbotwbnwod6e3oi

Defect Prediction Model for AOP-based Software Development using Hybrid Fuzzy C-Means with Genetic Algorithm and K-Nearest Neighbors Classifier

Pankaj Kumar, S. K.
2016 International Journal of Applied Information Systems  
Third is a hybrid approach (i.e. combination of fuzzy c-means and genetic algorithms) have been performed.  ...  One is Fuzzy C-Means Clustering (FCM) approach and another is K-Nearest Neighbors (KNN) classifier technique, have been performed in real data.  ...  The ninth section concludes summarizing the contribution of the paper and future work directions.  ... 
doi:10.5120/ijais2016451579 fatcat:mbzitkphjjgercggzpxuywxtwy

Enhanced learning classifier system for robot navigation

P. Musilek, Sa Li, L. Wyard-Scott
2005 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Enhancements to the classification system are described and tested using a simulated robot and environment.  ...  This paper describes an enhanced learning classifier system used to evolve obstacle-avoidance rules used in mobile robot navigation.  ...  CONCLUSION AND FUTURE DIRECTIONS In this paper, a classifier system has been applied to mobile robot navigation.  ... 
doi:10.1109/iros.2005.1545150 dblp:conf/iros/MusilekLW05 fatcat:exr2apdaxje5rke77p375joina

What Is a Learning Classifier System? [chapter]

John H. Holland, Lashon B. Booker, Marco Colombetti, Marco Dorigo, David E. Goldberg, Stephanie Forrest, Rick L. Riolo, Robert E. Smith, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson
2000 Lecture Notes in Computer Science  
We asked "What is a Learning Classifier System" to some of the best-known researchers in the field. These are their answers.  ...  These and other questions related to classifier systems as a model of cognitive activity are an important direction for future research.  ...  Holland Classifier systems are intended as a framework that uses genetic algorithms to study learning in condition/action, rule-based systems.  ... 
doi:10.1007/3-540-45027-0_1 fatcat:5n2cfauk3jc4vbsbuue3v2jfum

Evolutionary Multiobjective Design of Fuzzy Rule-Based Systems

Hisao Ishibuchi
2007 2007 IEEE Symposium on Foundations of Computational Intelligence  
We also suggest some future research directions related to the evolutionary multiobjective design of fuzzy rule-based systems.  ...  Most of those tuning methods are based on learning algorithms of neural networks and/or evolutionary optimization techniques.  ...  FUTURE RESEARCH DIRECTIONS The following seem to be interesting future research directions related to evolutionary multiobjective design of fuzzy rule-based systems: (1) Interpretability measures: It is  ... 
doi:10.1109/foci.2007.372141 dblp:conf/foci/Ishibuchi07 fatcat:y27qitqkvzdnrdqn2y5esxp2s4

Adversarial Samples on Android Malware Detection Systems for IoT Systems

Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Hao Wang, Mohsen Guizani
2019 Sensors  
By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform  ...  With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased.  ...  Phan et al. proposed a GA-SVM model that can effectively improve classification performance based on genetic algorithm and SVM classifier [32] .  ... 
doi:10.3390/s19040974 fatcat:kn6lkidhhrhbjge2uaknrzvsnm

Systems biology data analysis methodology in pharmacogenomics

Andrei S Rodin, Grigoriy Gogoshin, Eric Boerwinkle
2011 Pharmacogenomics (London)  
understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.  ...  primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery.  ...  Grigoriy Gogoshin is supported by grants from PhRMA foundation and NIH 5R03LM009738, 3U01HG004402 and 5R01HL072810.  ... 
doi:10.2217/pgs.11.76 pmid:21919609 pmcid:PMC3482399 fatcat:r3ghjpybabf7jnyzzyqjatkgte

Learning Classifier Systems

Larry Bull, Pler Luca Lanzi, Wolfgang Stolzmann
2002 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems.  ...  This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities.  ...  ACKNOWLEDGEMENT Thanks to the members of the Learning Classifier System Group at UWE for many useful discussions during this work.  ... 
doi:10.1007/s005000100110 fatcat:dxn5lz4o6vgh3olkzx4azjlw2e

Plant Varieties Selection System

Kitti Koonsanit, Chuleerat Jaruskulchai, Poonsak Miphokasap, Apisit Eiumnoh
2011 Zenodo  
Our software is a front end of WEKA that provides fundamental data mining functions such as classify, clustering, and analysis functions.  ...  In the end of the day, meteorological data and environmental data becomes widely used such as plant varieties selection system.  ...  ACKNOWLEDGMENT The authors would like to thank the National Center for Genetic Engineering and Biotechnology (BIOTEC), a member of Ministry of Science and Technology of Thailand for providing dataset on  ... 
doi:10.5281/zenodo.1083109 fatcat:r7cvhvprlzfhvhbijabhj7bumu

Ensemble system based on genetic algorithm for stock market forecasting

Rafael Thomazi Gonzalez, Carlos Alberto Padilha, Dante Augusto Couto Barone
2015 2015 IEEE Congress on Evolutionary Computation (CEC)  
As a result, an Ensemble System based on Genetic Algorithm was designed to predict the weekly movement direction of Bovespa Index.  ...  Chapter 3 delves into the principles of Genetic Algorithm, starting with the theoretical foundations of the method, and proceeding through the main issues on designing this kind of algorithm.  ... 
doi:10.1109/cec.2015.7257276 dblp:conf/cec/GonzalezPB15 fatcat:6uag75gg45f5xprwmnq3wanrha

Multi-agent evolutionary systems for the generation of complex virtual worlds

J. Kruse, A. M. Connor
2015 EAI Endorsed Transactions on Creative Technologies  
This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive  ...  An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search  ...  These are created by the software system depending on the underlying model, for example random selection, interactive genetic algorithm without agent and interactive genetic algorithm with agent.  ... 
doi:10.4108/eai.20-10-2015.150099 fatcat:t6kc7puwvzaqbf4a23cwlhzape

Biocloud: A Systemic Review and Classification

Hongfeng Zhang, Vincent Y. Liu
2015 Journal of Software  
Current problems and future research direction of biocloud are discussed at the end this paper.  ...  ., genome analysis, systems biology, infection disease), indicating many researchers' interest.  ...  Future research direction Biocloud is in its infancy and is still developing.  ... 
doi:10.17706/jsw.10.6.695-712 fatcat:u4nojebenncgzbdohjp4e63nly
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