Book Chapters

Book Chapters (13)

Feature Extraction and Classification of EEG Signals. The Use of a Genetic Algorithm for an Application on Alertness Prediction

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Abstract

This chapter presents a method to automatically determine the alertness state of humans. Such a task is relevant in diverse domains, where a person is expected or required to be in a particular state of alertness. For instance, pilots, security personnel, or medical personnel are expected to be in a highly alert state, and this method could help to confirm this or detect possible problems. In this work, electroencephalographic (EEG) data from 58 subjects in two distinct vigilance states (state of high and low alertness) was collected via a cap with 58 electrodes. Thus, a binary classification problem is considered. To apply the proposed approach in a real-world scenario, it is necessary to build a prediction method that requires only a small number of sensors (electrodes), minimizing the total cost and maintenance of the system while also reducing the time required to properly setup the EEG cap. The approach presented in this chapter applies a preprocessing method for EEG signals based on the use of discrete wavelet decomposition (DWT) to extract the energy of each frequency in the signal. Then, a linear regression is performed on the energies of some of these frequencies and the slope of this regression is retained. A genetic algorithm (GA) is used to optimize the selection of frequencies on which the regression is performed and to select the best recording electrode. Results show that the proposed strategy derives accurate predictive models of alertness.

Published in
Guide to Brain-Computer Music Interfacing
Pages 191-220
Copyright
2014
ISBN
978-1-4471-6583-5
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Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

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  1. Leonardo Trujillo, Enrique Naredo and Yuliana Martínez. Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search. In Michael Emmerich, Andre Deutz, Oliver Schuetze, Thomas Bäck, Emilia Tantar, Alexandru-Adrian Tantar, Pierre Del Moral, Pierrick Legrand, Pascal Bouvry and Carlos A Coello (eds.). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in Intelligent Systems and Computing series, volume 227, Springer International Publishing, 2013, pages 293-305. URL, DOI BibTeX

    @incollection{,
    	year = 2013,
    	isbn = "978-3-319-01127-1",
    	booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV",
    	volume = 227,
    	series = "Advances in Intelligent Systems and Computing",
    	editor = "Emmerich, Michael and Deutz, Andre and Schuetze, Oliver and Bäck, Thomas and Tantar, Emilia and Tantar, Alexandru-Adrian and Moral, Pierre Del and Legrand, Pierrick and Bouvry, Pascal and Coello, Carlos A.",
    	doi = "10.1007/978-3-319-01128-8_19",
    	title = "Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search",
    	url = "http://dx.doi.org/10.1007/978-3-319-01128-8_19",
    	publisher = "Springer International Publishing",
    	keywords = "Bloat; Genetic Programming; Novelty Search",
    	author = "Trujillo, Leonardo and Naredo, Enrique and Martínez, Yuliana",
    	pages = "293-305"
    }
    

Abstract

Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions. Over the years, many theories regarding the causes of bloat have been proposed and a variety of bloat control methods have been developed. It seems that one of the underlying causes of bloat is the search for fitness; as the fitness-causes-bloat theory states, selective bias towards fitness seems to unavoidably lead the search towards programs with a large size. Intuitively, however, abandoning fitness does not appear to be an option. This paper, studies a GP system that does not require an explicit fitness function, instead it relies on behavior-based search, where programs are described by the behavior they exhibit and selective pressure is biased towards unique behaviors using the novelty search algorithm. Initial results are encouraging, the average program size of the evolving population does not increase with novelty search; i.e., bloat is avoided by focusing on novelty instead of quality.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV
Advances in Intelligent Systems and Computing
Pages 293-305
Volume 227
http://link.springer.com/chapter/10.1007%2F978-3-319-01128-8_19
Copyright
2013
ISSN
2194-5357
ISBN
978-3-319-01128-8
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Classification of EEG signals by an evolutionary algorithm

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  1. Laurent Vezard, Pierrick Legrand, Marie Chavent, Frederique Faita-Ainseba, Julien Clauzel and others. Classification of EEG signals by an evolutionary algorithm. In COMPSTAT 2012. 2012. BibTeX

    @inproceedings{vezard2012classification,
    	title = "Classification of EEG signals by an evolutionary algorithm",
    	author = "Vezard, Laurent and Legrand, Pierrick and Chavent, Marie and Faita-Ainseba, Frederique and Clauzel, Julien and others",
    	booktitle = "COMPSTAT 2012",
    	year = 2012
    }
    

Abstract

The goal is to predict the alertness of an individual by analyzing the brain activity through electroencephalographic data (EEG) captured with 58 electrodes. Alertness is characterized as a binary variable that can be in a normal or relaxed state. We collected data from 44 subjects before and after a relaxation practice, giving a total of 88 records. After a pre-processing step and data validation, we analyzed each record and discriminate the alertness states using our proposed slope criterion. Afterwards, several common methods for supervised classification (k nearest neighbors, decision trees -CART-, random forests, PLS and discriminant sparse PLS) were applied as predictors for the state of alertness of each subject. The proposed slope criterion was further refined using a genetic algorithm to select the most important EEG electrodes in terms of classification accuracy. Results shown that the proposed strategy derives accurate predictive models of alertness.

Published in
Advances in Knowledge Discovery and Management
Pages 137-158
Volume 4
http://hal.inria.fr/hal-00759439/
Copyright
2013
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**Advances in Adaptive Composite Filters for Object Recognition

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  1. Victor H Diaz-Ramirez, Leonardo Trujillo and Sergio Pinto-Fernandez. Advances in Adaptive Composite Filters for Object Recognition. . BibTeX

    @article{diazadvances,
    	title = "Advances in Adaptive Composite Filters for Object Recognition",
    	author = "Diaz-Ramirez, Victor H and Trujillo, Leonardo and Pinto-Fernandez, Sergio"
    }
    

Published in
Advances in Object Recognutiib Systems
Pages 91-110
Chapter 5
Volume 175
http://www.intechopen.com/books/advances-in-object-recognition-systems/advances-in-adaptive-composite-filters-for-object-recognition
Copyright
9 May 2012
ISBN
978-953-51-0598-5
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Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty

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  1. Edgar Galvan, Leonardo Trujillo, James McDermott and Ahmed Kattan. Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty. In Oliver Schütze, Carlos A Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Pierre Del Moral and Pierrick Legrand (eds.). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing series, volume 175, Springer Berlin Heidelberg, 2013, pages 41-56. URL, DOI BibTeX

    @incollection{,
    	year = 2013,
    	isbn = "978-3-642-31518-3",
    	booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II",
    	volume = 175,
    	series = "Advances in Intelligent Systems and Computing",
    	editor = "Schütze, Oliver and Coello Coello, Carlos A. and Tantar, Alexandru-Adrian and Tantar, Emilia and Bouvry, Pascal and Del Moral, Pierre and Legrand, Pierrick",
    	doi = "10.1007/978-3-642-31519-0_3",
    	title = "Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty",
    	url = "http://dx.doi.org/10.1007/978-3-642-31519-0_3",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Galvan, Edgar and Trujillo, Leonardo and McDermott, James and Kattan, Ahmed",
    	pages = "41-56"
    }
    

Abstract

It is commonly accepted that a mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. Locality has been classified in one of two categories: high and low locality. It is said that a representation has high locality if most genotypic neighbours correspond to phenotypic neighbours. The opposite is true for a representation that has low locality. It is argued that a representation with high locality performs better in evolutionary search compared to a representation that has low locality. In this work, we explore, for the first time, a study on Genetic Programming (GP) locality in continuous fitnessvalued cases. For this, we extended the original definition of locality (first defined and used in Genetic Algorithms using bitstrings) from genotype-phenotype mapping to the genotype-fitness mapping. Then, we defined three possible variants of locality in GP regarding neighbourhood. The experimental tests presented here use a set of symbolic regression problems, two different encoding and two different mutation operators. We show how locality can be studied in this type of scenarios (continuous fitness-valued cases) and that locality can successfully been used as a performance prediction tool.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
Advances in Intelligent Systems and Computing
Pages 41-56
Volume 175
http://link.springer.com/chapter/10.1007%2F978-3-642-31519-0_3
Copyright
2012
ISSN
2194-5357
ISBN
978-3-642-31519-0
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Analysis and Classification of Epilepsy Stages with Genetic Programming

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  1. Arturo Sotelo, Enrique Guijarro, Leonardo Trujillo, Luis Coria and Yuliana Martínez. Analysis and Classification of Epilepsy Stages with Genetic Programming. In Oliver Schütze, Carlos A Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Pierre Del Moral and Pierrick Legrand (eds.). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing series, volume 175, Springer Berlin Heidelberg, 2013, pages 57-70. URL, DOI BibTeX

    @incollection{,
    	year = 2013,
    	isbn = "978-3-642-31518-3",
    	booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II",
    	volume = 175,
    	series = "Advances in Intelligent Systems and Computing",
    	editor = "Schütze, Oliver and Coello Coello, Carlos A. and Tantar, Alexandru-Adrian and Tantar, Emilia and Bouvry, Pascal and Del Moral, Pierre and Legrand, Pierrick",
    	doi = "10.1007/978-3-642-31519-0_4",
    	title = "Analysis and Classification of Epilepsy Stages with Genetic Programming",
    	url = "http://dx.doi.org/10.1007/978-3-642-31519-0_4",
    	publisher = "Springer Berlin Heidelberg",
    	keywords = "Epilepsy Diagnosis; Genetic Programming; Classification",
    	author = "Sotelo, Arturo and Guijarro, Enrique and Trujillo, Leonardo and Coria, Luis and Martínez, Yuliana",
    	pages = "57-70"
    }
    

Abstract

Epilepsy is a widespread disorder that affects many individuals worldwide. For this reason much work has been done to develop computational systems that can facilitate the analysis and interpretation of the signals generated by a patients brain during the onset of an epileptic seizure. Currently, this is done by human experts since computational methods cannot achieve a similar level of performance. This paper presents a Genetic Programming (GP) based approach to analyze brain activity captured with Electrocorticogram (ECoG). The goal is to evolve classifiers that can detect the three main stages of an epileptic seizure. Experimental results show good performance by the GP-classifiers, evaluated based on sensitivity, specificity, prevalence and likelihood ratio. The results are unique within this domain, and could become a useful tool in the development of future treatment methods.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
Advances in Intelligent Systems and Computing
Pages 57-70
Volume 175
http://link.springer.com/chapter/10.1007%2F978-3-642-31519-0_4
Copyright
2012
ISSN
2194-5357
ISBN
978-3-642-31519-0

 

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Disparity Map Estimation by Combining Cost Volume Measures Using Genetic Programming

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  1. Enrique Naredo, Enrique Dunn and Leonardo Trujillo. Disparity Map Estimation by Combining Cost Volume Measures Using Genetic Programming. In Oliver Schütze, Carlos A Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Pierre Del Moral and Pierrick Legrand (eds.). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing series, volume 175, Springer Berlin Heidelberg, 2013, pages 71-86. URL, DOI BibTeX

    @incollection{,
    	year = 2013,
    	isbn = "978-3-642-31518-3",
    	booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II",
    	volume = 175,
    	series = "Advances in Intelligent Systems and Computing",
    	editor = "Schütze, Oliver and Coello Coello, Carlos A. and Tantar, Alexandru-Adrian and Tantar, Emilia and Bouvry, Pascal and Del Moral, Pierre and Legrand, Pierrick",
    	doi = "10.1007/978-3-642-31519-0_5",
    	title = "Disparity Map Estimation by Combining Cost Volume Measures Using Genetic Programming",
    	url = "http://dx.doi.org/10.1007/978-3-642-31519-0_5",
    	publisher = "Springer Berlin Heidelberg",
    	keywords = "Stereo Vision; Disparity Map; Genetic Programming",
    	author = "Naredo, Enrique and Dunn, Enrique and Trujillo, Leonardo",
    	pages = "71-86"
    }
    

Abstract

Stereo vision is one of the most active research areas in modern computer vision. The objective is to recover 3-D depth information from a pair of 2-D images that capture the same scene. This paper addresses the problem of dense stereo correspondence, where the goal is to determine which image pixels in both images are projections of the same 3-D point from the observed scene. The proposal in this work is to build a non-linear operator that combines three well known methods to derive a correspondence measure that allows us to retrieve a better approximation of the ground truth disparity of stereo image pair. To achieve this, the problem is posed as a search and optimization task and solved with genetic programming (GP), an evolutionary paradigm for automatic program induction. Experimental results on well known benchmark problems show that the combined correspondence measure produced by GP outperforms each standard method, based on the mean error and the percentage of bad pixels. In conclusion, this paper shows that GP can be used to build composite correspondence algorithms that exhibit a strong performance on standard tests.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
Advances in Intelligent Systems and Computing
Pages 71-86
Volume 175
http://link.springer.com/chapter/10.1007%2F978-3-642-31519-0_5
Copyright
2012
ISSN
2194-5357
ISBN
978-3-642-31519-0
Read more...

Improvement of the Backpropagation Algorithm Using (1+1) Evolutionary Strategies

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  1. JoséParra Galaviz, Patricia Melin and Leonardo Trujillo. Improvement of the Backpropagation Algorithm Using (1+1) Evolutionary Strategies. In Patricia Melin, Janusz Kacprzyk and Witold Pedrycz (eds.). Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence series, volume 312, Springer Berlin Heidelberg, 2010, pages 287-302. URL, DOI BibTeX

    @incollection{,
    	year = 2010,
    	isbn = "978-3-642-15110-1",
    	booktitle = "Soft Computing for Recognition Based on Biometrics",
    	volume = 312,
    	series = "Studies in Computational Intelligence",
    	editor = "Melin, Patricia and Kacprzyk, Janusz and Pedrycz, Witold",
    	doi = "10.1007/978-3-642-15111-8_18",
    	title = "Improvement of the Backpropagation Algorithm Using (1+1) Evolutionary Strategies",
    	url = "http://dx.doi.org/10.1007/978-3-642-15111-8_18",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Galaviz, JoséParra and Melin, Patricia and Trujillo, Leonardo",
    	pages = "287-302"
    }
    

Abstract

Currently, the standard in supervised Artificial Neural Networks (ANNs) research is to use the backpropagation (BP) algorithm or one of its improved variants, for training. In this chapter, we present an improvement to the most widely used BP learning algorithm using (1+1) evolutionary Strategy (ES), one of the most widely used artificial evolution paradigms. The goal is to provide a method that can adaptively change the main learning parameters of the BP algorithm in an unconstrained manner. The BP/ES algorithm we propose is simple to implement and can be used in combination with various improved versions of BP. In our experimental tests we can see a substantial improvement in ANN performance, in some cases a reduction of more than 50% in error for time series prediction on a standard benchmark test. Therefore, we believe that our proposal effectively combines the learning abilities of BP with the global search of ES to provide a useful tool that improves the quality of learning for BP-based methods.

Published in
Soft Computing for Recognition Based on Biometrics
Studies in Computational Intelligence
Pages 287-302
Chapter 10
Volume 312
http://link.springer.com/chapter/10.1007%2F978-3-642-15111-8_18
Copyright
2010
ISSN
1860-949X
ISBN
978-3-642-15111-8
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Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration

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  1. Mónica Beltrán, Patricia Melin and Leonardo Trujillo. Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration. In Oscar Castillo, Witold Pedrycz and Janusz Kacprzyk (eds.). Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control. Studies in Computational Intelligence series, volume 257, Springer Berlin Heidelberg, 2009, pages 185-201. URL, DOI BibTeX

    @incollection{,
    	year = 2009,
    	isbn = "978-3-642-04513-4",
    	booktitle = "Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control",
    	volume = 257,
    	series = "Studies in Computational Intelligence",
    	editor = "Castillo, Oscar and Pedrycz, Witold and Kacprzyk, Janusz",
    	doi = "10.1007/978-3-642-04514-1_10",
    	title = "Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration",
    	url = "http://dx.doi.org/10.1007/978-3-642-04514-1_10",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Beltrán, Mónica and Melin, Patricia and Trujillo, Leonardo",
    	pages = "185-201"
    }
    

Abstract

This chapter describes a modular neural network (MNN) with fuzzy integration for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community [59]. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature; however this problem has proven to be a very difficult task. In this work, we propose a MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined using a Sugeno fuzzy integral and a fuzzy inference system [65]. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.

Published in
Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control
Studies in Computational Intelligence
Pages 185-201
Chapter 10
Volume 257
http://link.springer.com/chapter/10.1007/978-3-642-04514-1_10
Copyright
2009
ISSN
1860-949X
ISBN
978-3-642-04514-1
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Detecting Scale-Invariant Regions Using Evolved Image Operators

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  1. Leonardo Trujillo and Gustavo Olague. Detecting Scale-Invariant Regions Using Evolved Image Operators. In Stefano Cagnoni (ed.). Evolutionary Image Analysis and Signal Processing. Studies in Computational Intelligence series, volume 213, Springer Berlin Heidelberg, 2009, pages 21-40. URL, DOI BibTeX

    @incollection{,
    	year = 2009,
    	isbn = "978-3-642-01635-6",
    	booktitle = "Evolutionary Image Analysis and Signal Processing",
    	volume = 213,
    	series = "Studies in Computational Intelligence",
    	editor = "Cagnoni, Stefano",
    	doi = "10.1007/978-3-642-01636-3_2",
    	title = "Detecting Scale-Invariant Regions Using Evolved Image Operators",
    	url = "http://dx.doi.org/10.1007/978-3-642-01636-3_2",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Trujillo, Leonardo and Olague, Gustavo",
    	pages = "21-40"
    }
    

Abstract

This chapter describes scale-invariant region detectors that are based on image operators synthesized through Genetic Programming (GP). Interesting or salient regions on an image are of considerable usefulness within a broad range of vision problems, including, but not limited to, stereo vision, object detection and recognition, image registration and content-based image retrieval. A GP-based framework is described where candidate image operators are synthesized by employing a fitness measure that promotes the detection of stable and dispersed image features, both of which are highly desirable properties. After a significant number of experimental runs, a plateau of maxima was identified within the search space that contained operators that are similar, in structure and/or functionality, to basic LoG or DoG filters. Two such operators with the simplest structure were selected and embedded within a linear scale space, thereby making scale-invariant feature detection a straightforward task. The proposed scale-invariant detectors exhibit a high performance on standard tests when compared with state-of-the-art techniques. The experimental results exhibit the ability of GP to construct highly reusable code for a well known and hard task when an appropriate optimization problem is framed.

Published in
Evolutionary Image Analysis and Signal Processing
Studies in Computational Intelligence
Pages 21-40
Chapter 2
Volume 213
http://link.springer.com/chapter/10.1007%2F978-3-642-01636-3_2
Copyright
2009
ISSN
1860-949X
ISBN
978-3-642-01636-3
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