CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods

Diferencias de género en los motivos de práctica deportiva de la juventud en España

En la última década se ha observado una evolución hacia comportamientos de ocio más pasivos en la juventud española (INJUVE, 2008). Su tasa de práctica deportiva se ha estabilizado, observándose una menor participación de las mujeres, y, aunque se concretan los motivos por los que la juventud practica deporte, no se especifican las diferencias entre hombres y mujeres en los motivos por los que practican deporte (García Ferrando, 2006). Profundizar en el conocimiento de las diferencias por las que chicas y chicos practican deporte puede posibilitar que las organizaciones deportivas adapten su oferta a las diferentes demandas, reduciendo las desigualdades en la participación entre mujeres y hombres. Es por ello, que el objetivo de este trabajo es analizar las diferencias entre los motivos para practicar deporte de los hombres y las mujeres jóvenes en Españ

Coevolution of Generative Adversarial Networks

Generative adversarial networks (GAN) became a hot topic, presenting impressive results in the field of computer vision. However, there are still open problems with the GAN model, such as the training stability and the hand-design of architectures. Neuroevolution is a technique that can be used to provide the automatic design of network architectures even in large search spaces as in deep neural networks. Therefore, this project proposes COEGAN, a model that combines neuroevolution and coevolution in the coordination of the GAN training algorithm. The proposal uses the adversarial characteristic between the generator and discriminator components to design an algorithm using coevolution techniques. Our proposal was evaluated in the MNIST dataset. The results suggest the improvement of the training stability and the automatic discovery of efficient network architectures for GANs. Our model also partially solves the mode collapse problem.Comment: Published in EvoApplications 201

Diferencias de género en las motivaciones para practicar actividades físico-deportivas en la vejez

El objetivo de este estudio es analizar las posibles diferencias de género en las motivaciones por las que las personas mayores practican actividades físico-deportivas. La metodología cuantitativa empleada ha sido entrevista telefónica asistida por ordenador (CATI) a una muestra representativa de la población de mayores en España (440 mujeres y 360 varones entre 65 y 79 años). En mujeres y hombres mayores se conjugan principalmente motivaciones de carácter extrínseco, relacionados con el cuidado y mejora de la salud (62,1% mujeres, 60% hombres), y de carácter intrínseco, como el gusto por la actividad (33,3% mujeres, 32% hombres). Las motivaciones de carácter social, como divertirse, ocupar el tiempo o la recomendación médica tienen una influencia mayor en mujeres mientras que los hombres se refieren a la importancia de la práctica anterior en la práctica actual. Los resultados sugieren que los programas de actividades físico-deportivas para personas mayores deben considerar estas diferencias

Improving translation initiation site and stop codon recognition by using more than two classes

Motivation: The recognition of translation initiation sites and stop codons is a fundamental part of any gene recognition program. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. These methods all use two classes, one of positive instances and another one of negative instances that are constructed using sequences from the whole genome. However, the features of the negative sequences differ depending on the position of the negative samples in the gene. There are differences depending on whether they are from exons, introns, intergenic regions or any other functional part of the genome. Thus, the positive class is fairly homogeneous, as all its sequences come from the same part of the gene, but the negative class is composed of different instances. The classifier suffers from this problem. In this article, we propose the training of different classifiers with different negative, more homogeneous, classes and the combination of these classifiers for improved accuracy. Results: The proposed method achieves better accuracy than the best state-of-the-art method, both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. The method is tested on the whole human genome. The results for recognizing both translation initiation sites and stop codons indicated improvements in the rates of both false-negative results (FN) and false-positive results (FP). On an average, for translation initiation site recognition, the false-negative ratio was reduced by 30.2% and the FP ratio decreased by 10.9%. For stop codon prediction, FP were reduced by 41.4% and FN by 31.7%. Availability and implementation: The source code is licensed under the General Public License and is thus freely available. The datasets and source code can be obtained from http://cib.uco.es/site-recognition. Contact: [email protected]

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features

Factores asociados a la actividad física y el sedentarismo en los estilos de vida de la juventud española

La comunicación presenta los resultados de un estudio realizado por la Universidad Pablo de Olavide, la Universidad Politécnica de Madrid y la Universidad Europea de Madrid, sobre las tendencias culturales en el abandono de la actividad física y el deporte entre la juventud española. El estudio, que está realizándose actualmente gracias a una ayuda concedida por el Consejo Superior de Deportes (CSD) (Ref. 007/UPB10/12), analiza cómo la adopción de ese nuevo estilo de vida (sedentario) que está progresivamente afianzándose entre la juventud española puede influir de una manera u otra en la percepción subjetiva de la salud y el bienestar entre este grupo de población

Influencia de los estilos de vida físicamente activos y sedentarios en la percepción subjetiva de la salud y el bienestar de la juventud española

La presente comunicación se enmarca en un estudio financiado por el Consejo Superior de Deportes (CSD) (Ref. 007/UPB10/12). Uno de los objetivos específicos del estudio, en el que se basa la comunicación, es ampliar el conocimiento sobre la influencia de los estilos de vida físicamente activos y sedentarios en la percepción subjetiva de la salud y el bienestar de la juventud española

Barreras para la práctica de actividades físico-deportivas de las mujeres y hombres adultos de la Comunidad de Madrid

La comunicación presenta parte de los resultados del estudio DEP2010-19801 del Plan Nacional I+D+i 2010-2013. El objetivo de esta comunicación en identificar las diferentes barreras para practicar actividad física que presentan las mujeres y hombres adultos de la CCM

Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification

Cooperative coevolution is a promising method for training neural networks which is also known as cooperative neuro-evolution. Cooperative neuro-evolution has been used for pattern classification, time series prediction and global optimisation problems. In the past, competitive island based cooperative coevolution has been proposed that employed different instances of problem decomposition methods for competition. Neuro-evolution has limitations in terms of training time although they are known as global search methods. Backpropagation algorithm employs gradient descent which helps in faster convergence which is needed for neuro-evolution. Backpropagation suffers from premature convergence and its combination with neuro-evolution can help eliminate the weakness of both the approaches. In this paper, we propose a competitive island cooperative neuro-evolutionary method that takes advantage of the strengths of gradient descent and neuro-evolution. We use feedforward neural networks on benchmark pattern classification problems to evaluate the performance of the proposed algorithm. The results show improved performance when compared to related methods