A comparison of predictive measures of problem difficulty for classification with Genetic Programming

Published in Journal Articles


In the field of Genetic Programming (GP) a question exists that is difficult to solve; how can problem difficulty be determined? In
this paper the overall goal is to develop predictive tools that estimate how difficult a problem is for GP to solve. Here we analyse 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. The second 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 a GP system. These predictive variables are domain specific thus problems are described in the context of the problem domain. This paper compares an EI, the Negative Slope Coefficient, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of GP classifiers. Conversely, the PEP models show a high correlation with GP performance. It appears that while an EI estimates the difficulty of a search, it does not necessarily capture the difficulty of the underlying problem. However, while PEP models treat GP as a computational black-box, they can produce accurate performance predictions.

  1. Yuliana Martínez, Leonardo Trujillo, Galvan Galván-López and Pierrick Legrand. A comparison of predictive measures of problem difficulty for classification with Genetic Programming. In ERA 2012. 2012. BibTeX

    	title = "A comparison of predictive measures of problem difficulty for classification with Genetic Programming",
    	author = "Mart\'{i}nez, Yuliana and Trujillo, Leonardo and Galv\'{a}n-L\'{o}pez, Galvan and Legrand, Pierrick",
    	booktitle = "ERA 2012",
    	year = 2012


Identification of epilepsy stages from ECoG using genetic programming classifiers

Published in Journal Articles


Objective: Epilepsy is a common neurological disorder, for which a great deal of research has been devoted to analyze and characterize brain activity during seizures. While this can be done by a human expert, automatic methods still lag behind. This paper analyzes neural activity captured with Electrocorticogram (ECoG), recorded through intracranial implants from Kindling model test subjects. The goal is to automatically identify the main seizure stages: Pre-Ictal, Ictal and Post-Ictal. While visually differentiating each stage can be done by an expert if the complete time-series is available, the goal here is to automatically identify the corresponding stage of short signal segments.

Methods and materials: The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days.

Results: Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average classification error of 14.55%, sensitivity values between 0.65 and 0.97, and specificity values between 0.86 and 0.94.

Conclusions: The proposed approach achieves good results for stage identification, particularly when compared with previous works that focus on this task. The results show that the problem of intra-class classification can be solved with a low error, and high sensitivity and specificity. Moreover, the limitations of the approach are identified and good operating configurations can be proposed based on the results.

  1. Arturo Sotelo, Enrique Guijarro, Leonardo Trujillo, Luis N Coria and Yuliana Martínez. Identification of epilepsy stages from ECoG using genetic programming classifiers. Computers in Biology and Medicine 43(11):1713 - 1723, 2013. URL, DOI BibTeX

    	title = "Identification of epilepsy stages from \{ECoG\} using genetic programming classifiers",
    	journal = "Computers in Biology and Medicine",
    	volume = 43,
    	number = 11,
    	pages = "1713 - 1723",
    	year = 2013,
    	doi = "http://dx.doi.org/10.1016/j.compbiomed.2013.08.016",
    	url = "http://www.sciencedirect.com/science/article/pii/S001048251300231X",
    	author = "Arturo Sotelo and Enrique Guijarro and Leonardo Trujillo and Luis N. Coria and Yuliana Mart\'{i}nez"
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