Conferences Papers

Conferences Papers (39)

Using Semantics in the Selection Mechanism in Genetic Programming: a Simple Method for Promoting Semantic Diversity

by
  1. Y Martínez, E Naredo, L Trujillo and E Galván-López. Searching for novel regression functions. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 16-23. DOI BibTeX

    @inproceedings{6557548,
    	author = "Y. Martínez and E. Naredo and L. Trujillo and E. Galván-López",
    	booktitle = "2013 IEEE Congress on Evolutionary Computation",
    	title = "Searching for novel regression functions",
    	year = 2013,
    	volume = "",
    	number = "",
    	pages = "16-23",
    	keywords = "genetic algorithms;regression analysis;search problems;NS;behavior-based search;domain-specific descriptor;evolutionary computation;genetic programming;novelty search algorithm;regression functions;semantics-based GP algorithms;symbolic regression;Context;Robots;Search problems;Semantics;Sociology;Statistics;Vectors;Behavior-based Search;Genetic Programming;Novelty Search;Symbolic Regression",
    	doi = "10.1109/CEC.2013.6557548",
    	issn = "1089-778X",
    	month = "June"
    }
    
Abstract

Research on semantics in Genetic Programming (GP) has increased over the last number of years. Results in this area clearly indicate that its use in GP considerably increases performance. Many of these semantic-based approaches rely on a trial-and-error method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossover-semantics based - CSB). This, in consequence, has a major drawback: these methods could evaluate thousands of nodes, resulting in paying a high computational cost, while attempting to improve performance by promoting semantic diversity. In this work, we propose a simple and computationally inexpensive method, named semantics in selection, that eliminates the computational cost observed in CSB approaches. We tested this approach in 14 GP problems, including continuous- and discrete-valued fitness functions, and compared it against a traditional GP and a CSB approach. Our results are equivalent, and in some cases, superior than those found by the CSB approach, without the necessity of using a “brute force” mechanism.

Published in
IEEE Congress on Evolutionary Computation (CEC)
Pages 2972 - 2979
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557931&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6557931
Date of conference
20-23 June 2013
E-ISBN
978-1-4799-0452-5
ISBN
978-1-4799-0453-2
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Detecting mental states of alertness with genetic algorithm variable selection

by
  1. E Galván-López, B Cody-Kenny, L Trujillo and A Kattan. Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 2972-2979. DOI BibTeX

    @inproceedings{6557931,
    	author = "E. Galván-López and B. Cody-Kenny and L. Trujillo and A. Kattan",
    	booktitle = "2013 IEEE Congress on Evolutionary Computation",
    	title = "Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity",
    	year = 2013,
    	volume = "",
    	number = "",
    	pages = "2972-2979",
    	keywords = "genetic algorithms;GP;brute force mechanism;crossover operator;crossover-semantics based operator;genetic programming;selection mechanism;semantic diversity promotion;semantic-based approach;semantics-in-selection method;trial-and-error method;Computational efficiency;Context;Genetic programming;Semantics;Sociology;Statistics;Vectors",
    	doi = "10.1109/CEC.2013.6557931",
    	issn = "1089-778X",
    	month = "June"
    }
    
Abstract

The objective of the present work is to develop a method able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state. For instance, pilots or medical staffs are expected to be in a highly alert state, and this method could help to detect possible problems. In this paper, an approach is developed to predict the state of alertness (“normal” or “relaxed”) from the study of electroencephalographic signals (EEG) collected with a limited number of electrodes. The EEG of 58 participants in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select an optimal subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the participants. Different subset sizes were investigated and the best result was obtained by considering 9 electrodes (correct classification rate of 73.68%).

Published in
IEEE Congress on Evolutionary Computation (CEC)
Pages 1247-1254
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557708&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6552460%2F6557545%2F06557708.pdf%3Farnumber%3D6557708
Date of conference
20-23 June 2013
E-ISBN
978-1-4799-0452-5
ISBN
978-1-4799-0453-2
Read more...

Fireworks: Evolutionary art project based on EvoSpace-Interactive

by
  1. L Vézard, M Chavent, P Legrand, F Faïta-Aïnseba and L Trujillo. Detecting mental states of alertness with genetic algorithm variable selection. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 1247-1254. DOI BibTeX

    @inproceedings{6557708,
    	author = "L. Vézard and M. Chavent and P. Legrand and F. Faïta-Aïnseba and L. Trujillo",
    	booktitle = "2013 IEEE Congress on Evolutionary Computation",
    	title = "Detecting mental states of alertness with genetic algorithm variable selection",
    	year = 2013,
    	volume = "",
    	number = "",
    	pages = "1247-1254",
    	keywords = "electroencephalography;genetic algorithms;medical signal detection;CSP;EEG;LDA;common spatial pattern;decision rule;electrode optimal subset selection;electroencephalographic signals;genetic algorithm variable selection;linear discriminant analysis;mental states alertness detection;vigilance mental states;Bioinformatics;Data acquisition;Electrodes;Electroencephalography;Genetic algorithms;Genomics;Hidden Markov models",
    	doi = "10.1109/CEC.2013.6557708",
    	issn = "1089-778X",
    	month = "June"
    }
    
Abstract

This paper presents a collaborative-interactive evolutionary algorithm (C-IEA) that evolves artistic animations and is executed on the web. The application is called Fireworks, since the animations that are produced are similar to an elaborate fireworks display. The system is built using the EvoSpace platform for distributed and asynchronous evolutionary algorithms. EvoSpace provides a central repository for the evolving population and remote clients, called EvoWorkers, that interact with the system to perform fitness evaluation using an interactive approach. The artistic animations are coded using the Processing programming language that facilitates rapid development of computer graphics applications for artists and graphic designers. The system promotes user collaboration and interaction by allowing many users to participate in population evaluation and because the system incorporates social networking. Initial results show that the proposed C-IEA can allow users to produce interesting artistic artifacts that incorporate preferences from several users, evolving dynamic animations that are unique within evolutionary art.

Published in
IEEE Congress on Evolutionary Computation (CEC)
Pages 2871-2878
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557918&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6557918
Date of conference
20-23 June 2013
E-ISBN
978-1-4799-0452-5
ISBN
978-1-4799-0453-2
Read more...

Is there a free lunch for cloud-based evolutionary algorithms?

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  1. L Trujillo, M García-Valdez, F Fernández-de-Vega and J J Merelo. Fireworks: Evolutionary art project based on EvoSpace-interactive. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 2871-2878. DOI BibTeX

    @inproceedings{6557918,
    	author = "L. Trujillo and M. García-Valdez and F. Fernández-de-Vega and J. J. Merelo",
    	booktitle = "2013 IEEE Congress on Evolutionary Computation",
    	title = "Fireworks: Evolutionary art project based on EvoSpace-interactive",
    	year = 2013,
    	volume = "",
    	number = "",
    	pages = "2871-2878",
    	keywords = "art;computer animation;evolutionary computation;social networking (online);user interfaces;C-IEA;EvoSpace-Interactive platform;EvoWorkers;Fireworks application;Processing programming language;artistic animation;collaborative-interactive evolutionary algorithm;computer graphics application;dynamic animation;evolutionary algorithm;evolutionary art project;social networking;user collaboration;user interaction;Animation;Collaboration;Evolutionary computation;Servers;Silicon;Sociology;Statistics;Distributed algorithms;cloud computing;interactive evolutionary algorithm;linear genetic programming",
    	doi = "10.1109/CEC.2013.6557918",
    	issn = "1089-778X",
    	month = "June"
    }
    
Abstract

In this paper we present a distributed evolutionary algorithm that uses exclusively cloud services. This presents certain advantages, such as avoiding the acquisition of expensive resources, but at the same time presents the problem of choice between different services at different levels (infrastructure, platform, software) and, finally the actual scalability that can be achieved in a real distributed evolutionary algorithm. These issues are addressed by creating a pure-cloud version of EvoSpace, a pool-based evolutionary algorithm previously presented by the authors. EvoSpace is tested using the free tier of two services (one for the pool and other for the clients) and also the paying tier, and speedup is measured and its limits assessed. In general, this paper proves that a low-cost distributed evolutionary algorithm system can be created using cloud services that can be set up in very short time, but that major efficiency improvements can be obtained by switching to the non-free tier, giving another twist to the famous phrase “there is no free lunch”. We also show that using a pool-based algorithm allows to use cloud services more efficiently (and dynamically) than a static or synchronous service.

Published in
IEEE Congress on Evolutionary Computation (CEC)
Pages 1255 - 1262
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557709&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6557709
Date of conference
20-23 June 2013
E-ISBN
978-1-4799-0452-5
ISBN
978-1-4799-0453-2
Read more...

A comparative study of an evolvability indicator and a predictor of expected performance for genetic programming

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  1. Leonardo Trujillo, Yuliana Mart\'ınez, Edgar Galván López and Pierrick Legrand. A Comparative Study of an Evolvability Indicator and a Predictor of Expected Performance for Genetic Programming. In Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation. 2012, 1489–1490. URL, DOI BibTeX

    @inproceedings{Trujillo:2012:CSE:2330784.2331006,
    	author = "Trujillo, Leonardo and Mart\'{\i}nez, Yuliana and Galv\'{a}n L\'{o}pez, Edgar and Legrand, Pierrick",
    	title = "A Comparative Study of an Evolvability Indicator and a Predictor of Expected Performance for Genetic Programming",
    	booktitle = "Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation",
    	series = "GECCO '12",
    	year = 2012,
    	isbn = "978-1-4503-1178-6",
    	location = "Philadelphia, Pennsylvania, USA",
    	pages = "1489--1490",
    	numpages = 2,
    	url = "http://doi.acm.org/10.1145/2330784.2331006",
    	doi = "10.1145/2330784.2331006",
    	acmid = 2331006,
    	publisher = "ACM",
    	address = "New York, NY, USA",
    	keywords = "classification, genetic programming, performance prediction"
    }
    
Abstract

An open question within Genetic Programming (GP) is how to characterize problemdifficulty. The goal is to develop predictive tools that estimate how difficult a problemis for GP to solve. Here we consider two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. Examples of EIs are Fitness Distance Correlation (FDC) and Negative Slope Coefficient (NSC). The second group are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of GP. This paper compares an EI, the NSC, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of the GP classifiers. Conversely, the PEP models show a high correlation with GP performance.

Published in
GECCO Companion '12 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion
Pages 1489-1490
http://dl.acm.org/citation.cfm?id=2331006
Date of conference
07 - 11 July 2012
ISBN
978-1-4503-1178-6
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An empirical study of functional complexity as an indicator of overfitting in Genetic Programming

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  1. Leonardo Trujillo, Yuliana Mart\'ınez and Patricia Melin. How Many Neurons?: A Genetic Programming Answer. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation. 2011, 175–176. URL, DOI BibTeX

    @inproceedings{Trujillo:2011:MNG:2001858.2001956,
    	author = "Trujillo, Leonardo and Mart\'{\i}nez, Yuliana and Melin, Patricia",
    	title = "How Many Neurons?: A Genetic Programming Answer",
    	booktitle = "Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation",
    	series = "GECCO '11",
    	year = 2011,
    	isbn = "978-1-4503-0690-4",
    	location = "Dublin, Ireland",
    	pages = "175--176",
    	numpages = 2,
    	url = "http://doi.acm.org/10.1145/2001858.2001956",
    	doi = "10.1145/2001858.2001956",
    	acmid = 2001956,
    	publisher = "ACM",
    	address = "New York, NY, USA",
    	keywords = "artificial neural networks, genetic programming"
    }
    
Abstract

Recently, it has been stated that the complexity of a solution is a good indicator of the amount of overfitting it incurs. However, measuring the complexity of a program, in Genetic Programming, is not a trivial task. In this paper, we study the functional complexity and how it relates with overfitting on symbolic regression problems. We consider two measures of complexity, Slope-based Functional Complexity, inspired by the concept of curvature, and Regularity-based Functional Complexity based on the concept of Hölderian regularity. In general, both complexity measures appear to be poor indicators of program overfitting. However, results suggest that Regularity-based Functional Complexity could provide a good indication of overfitting in extreme cases.

Published in
Proceedings of the 14th European Conference on Genetic Programming
Volume 6621
Pages 262-273
http://link.springer.com/chapter/10.1007%2F978-3-642-20407-4_23
Date of conference
27-29 Abril 2011
ISSN
0302-9743
ISBN
978-3-642-20407-4
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Estimating classifier performance with Genetic Programming

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  1. Leonardo Trujillo, Yuliana Mart\'ınez, Edgar Galván-López and Pierrick Legrand. Predicting Problem Difficulty for Genetic Programming Applied to Data Classification. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. 2011, 1355–1362. URL, DOI BibTeX

    @inproceedings{Trujillo:2011:PPD:2001576.2001759,
    	author = "Trujillo, Leonardo and Mart\'{\i}nez, Yuliana and Galv\'{a}n-L\'{o}pez, Edgar and Legrand, Pierrick",
    	title = "Predicting Problem Difficulty for Genetic Programming Applied to Data Classification",
    	booktitle = "Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation",
    	series = "GECCO '11",
    	year = 2011,
    	isbn = "978-1-4503-0557-0",
    	location = "Dublin, Ireland",
    	pages = "1355--1362",
    	numpages = 8,
    	url = "http://doi.acm.org/10.1145/2001576.2001759",
    	doi = "10.1145/2001576.2001759",
    	acmid = 2001759,
    	publisher = "ACM",
    	address = "New York, NY, USA",
    	keywords = "classification, genetic programming, performance prediction"
    }
    
Abstract

A fundamental task that must be addressed before classifying a set of data, is that of choosing the proper classification method. In other words, a researcher must infer which classifier will achieve the best performance on the classification problem in order to make a reasoned choice. This task is not trivial, and it is mostly resolved based on personal experience and individual preferences. This paper presents a methodological approach to produce estimators of classifier performance, based on descriptive measures of the problem data. The proposal is to use Genetic Programming (GP) to evolve mathematical operators that take as input descriptors of the problem data, and output the expected error that a particular classifier might achieve if it is used to classify the data. Experimental tests show that GP can produce accurate estimators of classifier performance, by evaluating our approach on a large set of 500 two-class problems of multimodal data, using a neural network for classification. The results suggest that the GP approach could provide a tool that helps researchers make a reasoned decision regarding the applicability of a classifier to a particular problem.

Published in
Proceedings of the 14th European Conference on Genetic Programming
Volume 6621
Pages 274-285
http://link.springer.com/chapter/10.1007%2F978-3-642-20407-4_24
Date of conference
27-29 Abril 2011
ISSN
0302-9743
ISBN
978-3-642-20406-7
Read more...

How many neurons?: a genetic programming answer

by
  1. Leonardo Trujillo, Yuliana Mart\'ınez and Patricia Melin. Estimating Classifier Performance with Genetic Programming. In Proceedings of the 14th European Conference on Genetic Programming. 2011, 274–285. URL BibTeX

    @inproceedings{Trujillo:2011:ECP:2008307.2008333,
    	author = "Trujillo, Leonardo and Mart\'{\i}nez, Yuliana and Melin, Patricia",
    	title = "Estimating Classifier Performance with Genetic Programming",
    	booktitle = "Proceedings of the 14th European Conference on Genetic Programming",
    	series = "EuroGP'11",
    	year = 2011,
    	isbn = "978-3-642-20406-7",
    	location = "Torino, Italy",
    	pages = "274--285",
    	numpages = 12,
    	url = "http://dl.acm.org/citation.cfm?id=2008307.2008333",
    	acmid = 2008333,
    	publisher = "Springer-Verlag",
    	address = "Berlin, Heidelberg"
    }
    
Abstract

The goal of this paper is to derive predictive models that take as input a description of a problem and produce as output an estimate of the optimal number of hidden nodes in an Artificial Neural Network (ANN). We call such computational tools Direct Estimators of Neural Network Topology (DENNT), an use Genetic Programming (GP) to evolve them. The evolved DENNTs take as input statistical and complexity descriptors of the problem data, and output an estimate of the optimal number of hidden neurons.

Published in
GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Pages 175-176
http://dl.acm.org/citation.cfm?id=2001956&dl=ACM&coll=DL&CFID=249301493&CFTOKEN=11015299
Date of conference
12 - 16 July 2011
ISBN
978-1-4503-0690-4
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Predicting problem difficulty for genetic programming applied to data classification

by
  1. Leonardo Trujillo, Sara Silva, Pierrick Legrand and Leonardo Vanneschi. An Empirical Study of Functional Complexity As an Indicator of Overfitting in Genetic Programming. In Proceedings of the 14th European Conference on Genetic Programming. 2011, 262–273. URL BibTeX

    @inproceedings{Trujillo:2011:ESF:2008307.2008332,
    	author = "Trujillo, Leonardo and Silva, Sara and Legrand, Pierrick and Vanneschi, Leonardo",
    	title = "An Empirical Study of Functional Complexity As an Indicator of Overfitting in Genetic Programming",
    	booktitle = "Proceedings of the 14th European Conference on Genetic Programming",
    	series = "EuroGP'11",
    	year = 2011,
    	isbn = "978-3-642-20406-7",
    	location = "Torino, Italy",
    	pages = "262--273",
    	numpages = 12,
    	url = "http://dl.acm.org/citation.cfm?id=2008307.2008332",
    	acmid = 2008332,
    	publisher = "Springer-Verlag",
    	address = "Berlin, Heidelberg"
    }
    
Abstract

During the development of applied systems, an important problem that must be addressed is that of choosing the correct tools for a given domain or scenario. This general task has been addressed by the genetic programming (GP) community by attempting to determine the intrinsic difficulty that a problem poses for a GP search. This paper presents an approach to predict the performance of GP applied to data classification, one of themost common problems in computer science. The novelty of the proposal is to extract statistical descriptors and complexity descriptors of the problem data, and from these estimate the expected performance of a GP classifier. We derive two types of predictive models: linear regression models and symbolic regression models evolved with GP. The experimental results show that both approaches provide good estimates of classifier performance, using synthetic and real-world problems for validation. In conclusion, this paper shows that it is possible to accurately predict the expected performance of a GP classifier using a set of descriptors that characterize the problem data.

Published in
GECCO '11 Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pages 1355-1362
http://ulir.ul.ie/handle/10344/2880
Date of conference
12 - 16 July 2011
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The Estimation of Hölderian Regularity using Genetic Programming

by
  1. Leonardo Trujillo, Pierrick Legrand and Jacques Lévy-Véhel. The Estimation of HöLderian Regularity Using Genetic Programming. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. 2010, 861–868. URL, DOI BibTeX

    @inproceedings{Trujillo:2010:EHR:1830483.1830641,
    	author = "Trujillo, Leonardo and Legrand, Pierrick and L{\'e}vy-V{\'e}hel, Jacques",
    	title = {The Estimation of H\"{o}Lderian Regularity Using Genetic Programming},
    	booktitle = "Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation",
    	series = "GECCO '10",
    	year = 2010,
    	isbn = "978-1-4503-0072-8",
    	location = "Portland, Oregon, USA",
    	pages = "861--868",
    	numpages = 8,
    	url = "http://doi.acm.org/10.1145/1830483.1830641",
    	doi = "10.1145/1830483.1830641",
    	acmid = 1830641,
    	publisher = "ACM",
    	address = "New York, NY, USA",
    	keywords = "genetic programming, h?lder exponent, signal regularity"
    }
    
Abstract

This paper presents a Genetic Programming (GP) approach to synthesize estimators for the pointwise Hölder exponent in 2D signals. It is known that irregularities and singularities are the most salient and informative parts of a signal. Hence, explicitly measuring these variations can be important in various domains of signal processing. The pointwise Hölder exponent provides a characterization of these types of features. However, current methods for estimation cannot be considered to be optimal in any sense. Therefore, the goal of this work is to automatically synthesize operators that provide an estimation for the Hölderian regularity in a 2D signal. This goal is posed as an optimization problem in which we attempt to minimize the error between a prescribed regularity and the estimated regularity given by an image operator. The search for optimal estimators is then carried out using a GP algorithm. Experiments confirm that the GP-operators produce a good estimation of the Hölder exponent in images of multifractional Brownian motions. In fact, the evolved estimators significantly outperform a traditional method by as much as one order of magnitude. These results provide further empirical evidence that GP can solve difficult problems of applied mathematics.

Published in
GECCO '10 Proceedings of the 12th annual conference on Genetic and evolutionary computation
Pages 861-868
http://dl.acm.org/citation.cfm?id=1830483.1830641
Date of conference
07 - 11 July 2010
ISBN
978-1-4503-0072-8
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