Juan J. Merelo

Parallel Problem Solving from Nature PPSN VI

Spatially structured evolutionary algorithms (EAs) have shown to be endowed with useful features for global optimization. Distributed EAs (dEA) and cellular EAs (cEA) are two of the most widely known types of structured algorithms. In this paper we deal with cellular EAs. Two important parameters guiding the search in a cEA are the population topology and the neighborhood defined on it. Here we first review some theoretical results which show that a cEA with a 2D grid can be easily tuned to shift from exploration to exploitation. We initially make a study on the relationship between the topology and the neighborhood by defining a ratio measure between they two. Then, we encompass a set of tests aimed at discovering the performance that different ratio values have on different classes of problems. We find out that, with the same neighborhood, rectangular grids have some advantages in multimodal and epistatic problems, while square ones are more efficient for solving deceptive problems and for simple function optimization. Finally, we propose and study a cEA in which the ratio is dynamically changed.

Proteinotopic feature maps

Abstract In this paper a system based on Kohonens SOM (Self-Organizing Map) for protein classification according to Circular Dichroism (CD) spectra is described. As a result, proteins with different secondary structures are clearly separated through a completely unsupervised training process. The algorithm is able to extract features from a high-dimensional vector (CD spectra) and map it to a 2-dimensional network. A new measure, called distortion, has been introduced to test SOM performance. Distortion can be used to fine tune and optimize some of the parameters of the SOM algorithm.

SOMCD: Method for evaluating protein secondary structure from UV circular dichroism spectra

This article presents SOMCD, an improved method for the evaluation of protein secondary structure from circular dichroism spectra, based on Kohonens self‐organizing maps (SOM). Protein circular dichroism (CD) spectra are used to train a SOM, which arranges the spectra on a two‐dimensional map. Location in the map reflects the secondary structure composition of a protein. With SOMCD, the prediction of β‐turn has been included. The number of spectra in the training set has been increased, and it now includes 39 protein spectra and 6 reference spectra. Finally, SOM parameters have been chosen to minimize distortion and make the network produce clusters with known properties. Estimation results show improvements compared with the previous version, K2D, which, in addition, estimated only three secondary structure components; the accuracy of the method is more uniform over the different secondary structures. Proteins 2001;42:460–470.

Statistical analysis of the main parameters involved in the design of a genetic algorithm

Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover, mutation, and selection operators) manually. Nevertheless, when GA applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of a GA. The purpose of this study was to analyze the dynamics of GAs when confronted with modifications to the principal parameters that define them, taking into account the two main characteristics of GAs; their capacity for exploration and exploitation. Therefore, the dynamics of GAs have been analyzed from two viewpoints. The first is to study the best solution found by the system, i.e., to observe its capacity to obtain a local or global optimum. The second viewpoint is the diversity within the population of GAs; to examine this, the average fitness was calculated. The relevancy and relative importance of the parameters involved in GA design are investigated by using a powerful statistical tool, the analysis of the variance (ANOVA).

Statistical analysis of the parameters of a neuro-genetic algorithm

Interest in hybrid methods that combine artificial neural networks and evolutionary algorithms has grown in the last few years, due to their robustness and ability to design networks by setting initial weight values, by searching the architecture and the learning rule and parameters. This paper presents an exhaustive analysis of the G-Prop method, and the different parameters the method requires (population size, selection rate, initial weight range, number of training epochs, etc.) are determined. The paper also the discusses the influence of the application of genetic operators on the precision (classification ability or error) and network size in classification problems. The significance and relative importance of the parameters with respect to the results obtained, as well as suitable values for each, were obtained using the ANOVA (analysis of the variance). Experiments show the significance of parameters concerning the neural network and learning in the hybrid methods. The parameters found using this method were used to compare the G-Prop method both to itself with other parameter settings, and to other published methods.

Diversity Through Multiculturality: Assessing Migrant Choice Policies in an Island Model

The natural mate-selection behavior of preferring individuals which are somewhat (but not too much) different has been proved to increase the resistance to infection of the resulting offspring, and thus fitness. Inspired by these results we have investigated the improvement obtained from diversity induced by differences between individuals sent and received and the resident population in an island model, by comparing different migration policies, including our proposed multikulti methods, which choose the individuals that are going to be sent to other nodes based on the principle of multiculturality; the individual sent should be different enough to the target population, which will be represented through a proxy string (computed in several possible ways) in the emitting population. We have checked a set of policies following these principles on two discrete optimization problems of diverse difficulty for different sizes and number of nodes, and found that, in average or in median, multikulti policies outperform the usual policy of sending the best or a random individual; however, the size of this advantage changes with the number of nodes involved and the difficulty of the problem, tending to be greater as the number of nodes increases. The success of this kind of policies will be explained via the measurement of entropy as a representation of population diversity for the policies tested.

A Distributed Resource Evolutionary Algorithm Machine (DREAM)

This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open and scalable. The Distributed Resource Evolutionary Algorithm Machine (DREAM) will use existing hardware infrastructure in a more efficient manner, by utilising otherwise unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling and the modelling of economic and social behaviour.

A network analysis of the 2010 FIFA world cup champion team play

This paper analyzes the network of passes among the players of the Spanish team during the last FIFA World Cup 2010, where they emerged as the champion, with the objective of explaining the results obtained from the behavior at the complex network level. The team is considered a network with players as nodes and passes as (directed) edges. A temporal analysis of the resulting passes network is also done, looking at the number of passes, length of the chain of passes, and to network measures such as player centrality and clustering coefficient. Results of the last three matches (the decisive ones) indicate that the clustering coefficient of the pass network remains high, indicating the elaborate style of the Spanish team. The effectiveness of the opposing team in negating the Spanish game is reflected in the change of several network measures over time, most importantly in drops of the clustering coefficient and passing length/speed, as well as in their being able in removing the most talented players from the central positions of the network. Spain’s ability to restore their combinative game and move the focus of the game to offensive positions and talented players is shown to tilt the balance in favor of the Spanish team.

Bloat control operators and diversity in genetic programming: A comparative study

This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not derive from its influence on the genetic pool diversity.

Web newspaper layout optimization using simulated annealing

The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the clients computer configuration, and supports articles with different widths.