The recent boom of 5G networks, coupled with the escalating demand among users for higher-capacity connections, is presenting a formidable challenge. 5G networks encompass a diverse array of advantageous technologies, prominently including Software- Defined Networking (SDN) together with Network Function Virtualization (NFV), in which a key problem is the optimal development of Service Function Chaining (SFC). This paper presents two approaches based on Genetic Algorithms (GAs), for the optimal composition of SFCs focused on network pathways, and tailored for the service chain required in different 5G connections (user service requests). The approaches have been tested on different instances of the problem with topologies including 6, 19 and 52 nodes. The obtained results are promising, showing optimal paths inside simple and complex topologies for every connection as well as for multiple connections.

Evolution is a powerful problem-solving technique, extensively used for designing racing car controllers, but with a series of challenges: an evaluation function that can separate the best controllers from the rest, and a series of operators that can explore different possibilities in the controller search space. Within the context of the TORCS racing simulator, in this article, we introduce a selection policy based on competition calledGrand Prix Selection(GPS), which will be able to increase robustness by using something more realistic than solo race scores to select individuals. Additionally, we increase the exploitative power of this kind of selection via a BLX operator with continuously decreasing$alpha$. We compare these new selection and operator with hybrid approaches that apply GPS only part of the time, as well as other classical crossover operators. In general, experiments show that these combined improvements establish a new level of performance of evolved controllers, being able to beat both standard and previously evolved ones, as well as a high-ranked controller of TORCS competitions.

The following topics are dealt with: computer games; learning (artificial intelligence); virtual reality; computer aided instruction; Monte Carlo methods; neural nets; game theory; tree searching; artificial intelligence; and serious games (computing).

One of the main problems in the design through optimization of car racing bots is the inherent noise in the optimization process: besides the fact that the fitness is a heuristic based on what we think are the keys to success and as such just a surrogate for the ultimate objective, winning races, fitness itself is uncertain due to the stochastic behavior of racing conditions and the rest of the (simulated) racers. The fuzzy-based genetic controller for the car racing simulator TORCS we have defined in previous works is based on two fuzzy sub-controllers, one for deciding on the wheel steering angle and another to set the car target speed at the next simulation tick. They are both optimized by means of an Evolutionary Algorithm, which considers an already tested fitness function focused on the maximization of the average speed during the race and the minimization of the car damage. The noisy environment asks for keeping diversity high during evolution, that is why we have added a Blend Crossover (BLX-α) operator, which is, besides, able to exploit current results at the same time it explores. Additionally, we try to address uncertainty in selection by introducing a novel selection policy of parents based in races, where the individuals are grouped and compete against others in several races, so just the firsts ranked will remain in the population as parents. Several experiments have been conducted, testing the value of the different controllers. The results show that the combination of a dynamic BLX-α crossover operator plus the pole position selection policy clearly beats the rest of approaches. Moreover, in the comparison of this controller with one of the participants of the prestigious international Simulated Car Racing Championship, our autonomous driver obtains much better results than the opponent.

In this work, we present an original method based on evolutionary algorithms for generating basic structures for the physics-based game Angry Birds, with the ultimate objective of creating Angry Birds levels with the minimum number of constraints. We set out to evolve free-form structures, and this means searching in a larger space. In this paper, we test how using a physics engine enables us to evaluate much more levels than a game engine simulation. In order to do this, we compare the results of experiments using both types of simulators and propose fitness functions accordingly. Results show the execution time drastically drops from 5 hours to less than 20 minutes on average.

In the last year, thanks to the Ms. Pac-Man Versus Ghosts Competition, the game of Ms. Pac-Man has gained increasing attention from academics in the field of computational intelligence. In this paper, we contribute to this research stream by presenting a simple genetic algorithm with lexicographic ranking (GALR) for the optimization of flocking strategy-based ghost controllers. Flocking strategies are a paradigm for intelligent agents characterized by showing emergent behavior and for having very little computational and memory requirements, making them well suited for commercial applications and mobile devices. In particular, we study empirically the effect of optimizing homogeneous and heterogeneous teams. The computational analysis shows that the flocking strategy-based controllers generated by the proposed GALR outperform the ghost controllers included in the competition framework and some of those presented in the literature.

MOTIVATION: Self-organizing maps (SOMs) are readily available bioinformatics methods for clustering and visualizing high-dimensional data, provided that such biological information is previously transformed to fixed-size, metric-based vectors. To increase the usefulness of SOM-based approaches for the analysis of genomic sequence data, novel representation methods are required that automatically and objectively transform aligned nucleotide sequences into numeric vectors, dealing with both nucleotide ambiguity and gaps derived from sequence alignment. RESULTS: Six different codification variants based on Euclidean space, just like SOM processing, have been tested using two SOM models: the classical Kohonen's SOM and growing cell structures. They have been applied to two different sets of sequences: 32 sequences of small sub-unit ribosomal RNA from organisms belonging to the three domains of life, and 44 sequences of the reverse transcriptase region of the pol gene of human immunodeficiency virus type 1 belonging to different groups and sub-types. Our results show that the most important factor affecting the accuracy of sequence clustering is the assignment of an extra weight to the presence of alignment-derived gaps. Although each of the codification variants shows a different level of taxonomic consistency, the results are in agreement with sequence-based phylogenetic reconstructions and anticipate a broad applicability of this codification method.

Stories are not only painfully weaved by crafty writers in the solitude of their studios; they also have to be produced massively for non-player characters in the video game industry or tailored to particular tastes in personalized stories. However, the creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and backstories to create an engaging narrative. This work describes a general methodology to generate cohesive and coherent backstories where desired archetypes (universally accepted literary symbols) can emerge in complex stochastic systems. This methodology supports the modeling and parametrization of the agents, the environment where they will live and the desired literary setting. The use of a Genetic Algorithm (GA) is proposed to establish the parameter configuration that will lead to backstories that best fit the setting. Information extracted from a simulation can then be used to create the literary work. To demonstrate the adequacy of the methodology, we perform an implementation using a specific multi-agent system and evaluate the results, testing with three different literary settings.

KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.

In this work we address the problem of inventory and routing management in a retail chain. This involves the minimisation of two contradicting objectives, inventory holding costs and transportation costs, but which can be compounded in to a single one, the global costs. In previous work we addressed this using a single objective evolutionary algorithm but the duality inherent in the problem prompts us to consider a multiobjective approach; the aim is to determine what advantages each can bring. A number of experiments are carried out on several simulated and one real retail chain.

MasterMind is a puzzle in which a hidden string of symbols must be discovered by producing query strings which are compared with the hidden one; the result of this comparison (in terms of number of correct positions and colors) is fed back to the player that is trying to crack the code (codebreaker). Methods for solving this puzzle are usually compared in terms of the number of query strings (guesses) made and the total time needed to produce those strings. In this paper we focus on the latter by trying to find a combination of parameters that is, first, uniform and independent of the problem size, and second, adequate to find a fast solution that is, at the same time, good enough. The key to this combination of parameters will be the consistent set size, that is, the maximum number of combinations that are sought before being scored and played as a guess. Having found in previous papers that the consistent set size has an influence on speed, we will concentrate on small sizes and test them through two different scoring methods from literature: most parts and entropy to find out the influence of that parameter on the outcome and which method scales better. With this we try to find out which method and size yield the best results an are effectively able to? find solutions for sizes not approached so far in a reasonable time.

Solving the MasterMind puzzle, that is, finding out a hidden combination by using hints that tell you how close some strings are to that one is a combinatorial optimization problem that becomes increasingly difficult with string size and the number of symbols used in it. Since it does not have an exact solution, heuristic methods have been traditionally used to solve it; these methods scored each combination using a heuristic function that depends on comparing all possible solutions with each other. In this paper we first optimize the implementation of previous evolutionary methods used for the game of mastermind, obtaining up to a 40% speed improvement over them. Then we study the behavior of an entropy-based score, which has previously been used but not checked exhaustively and compared with previous solutions. The combination of these two strategies obtain solutions to the game of Mastermind that are competitive, and in some cases beat, the best solutions obtained so far. All data and programs have also been published under an open source license.

Pareto-based island model is a multi-colony distribution scheme recently presented for the resolution, by means of ant colony optimization algorithms, of bi-criteria problems. It yielded very promising results, but the model was implemented considering a unique Pareto-front-shaped unidirectional neighborhood migration topology, and a constant migration rate. In the present work two additional neighborhood topology schemes, and four different migration rates have been tested, considering the algorithm which obtained the best results in average in the model presentation article: MOACS (Multi-Objective Ant Colony System). Several experiments have been conducted, including statistical tests for better support the study. High values for the migration rate and the use of a bidirectional neighborhood migration topology yields the best results.

This work presents the design of a mobile gateway for independent life and e-health support. Technologies such as OSGi and Web Services have been used to deploy several services as an example, such as bio-medical parameter monitorization, alerts, and communication with a coordination center. A complete functional service which uses context-awareness has been explained and the benefits of using Service Oriented Architecture are explained. Finally, the lessons learned of this kind of development, which involves several types of systems, data, languages, and development groups, are discussed.

It is very important when search methods are being designed to know which parameters have the greatest influence on the behaviour and performance of the algorithm. To this end, algorithm parameters are commonly calibrated by means of either theoretic analysis or intensive experimentation. However, due to the importance of parameters and its effect on the results, finding appropriate parameter values should be carried out using robust tools to determine the way they operate and influence the results. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameters that are statistically significant. In this paper the ANOVA (ANalysis Of the VAriance) method is used to carry out an exhaustive analysis of an evolutionary algorithm method and the different parameters it requires. Following this idea, the significance and relative importance of the parameters regarding the obtained results, as well as suitable values for each of these, were obtained using ANOVA and post-hoc Tukey's Honestly Significant Difference tests on four well known function optimization problems. Through this statistical study we have verified the adequacy of parameter values available in the bibliography using parametric hypothesis tests.

This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three representative ways of extracting information from EEG signals: Hjorth features, wavelet transformation and symbolic representation. Feature selection was then used to evaluate the relevance of individual features from this set of methods. Stacked sequential learning uses a second-layer classifier to improve the classification by using previous and posterior first-layer predicted stages as additional features providing information to the model. Results show that both approaches enhance the sleep stage classification accuracy rate, thus leading to a closer approximation to the experts' opinion.

This work presents the experimental results obtained with a distributed computing system created by mapping an evolutionary algorithm to the CouchDB object store. The framework decouples the population from the evolutionary algorithm and -through the API that CouchDB provides- allows the distributed and asynchronous operation of clients written in different programming languages. In this paper we present tests which prove that the novel algorithm design still performs as good as a canonical evolutionary algorithm and discover what are the main issues concerning it, what kind of speedups should we expect, and how all this affects the fundamental evolutionary algorithms concepts.

Cloud computing is lately becoming a part of the tool-set that the scientist uses to perform compute-intensive tasks. In particular, cloud storage is an easy and convenient way of storing files that will be accessible over the Internet, but can also be used for distributing those files for performing computation on them. In this paper we describe how such a service commercialized by Dropbox is used for pool-based evolutionary algorithms. A prototype system is described and its performance measured over deceptive combinatorial optimization problems using two different substrates: WiFi and wired, finding that, for some type of problems and using commodity hardware, cloud storage systems can profitably be used as a platform for distributed evolutionary algorithms; however, performance is influenced by the type of underlying network. After introducing the method in a previous paper, in this paper we focus on measuring this influence, finding that wired is faster than WiFi for any number of nodes. We have also performed an experiment with a few more computers to see whether speedup keeps up with the number of nodes.

This paper describes an Evolutionary Algorithm for evolving the decision engine of a bot designed to play the Planet Wars game. This game, which has been chosen for the Google Artificial Intelligence Challenge in 2010, requires that the artificial player is able to deal with multiple objectives, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The decision engine of the bot is based on a set of rules that have been defined after an empirical study. Then, an Evolutionary Algorithm is used for tuning the set of constants, weights and probabilities that define the rules, and, therefore, the global behavior of the bot. The paper describes the Evolutionary Algorithm and the results attained by the decision engine when competing with other bots. The proposed bot defeated a baseline bot in most of the playing environments and obtained a ranking position in top-20% of the Google Artificial Intelligence competition.

The MasterMind puzzle is an interesting problem to be approached via evolutionary algorithms, since it is at the same time a constrained and a dynamic problem, and has eventually a single solution. In previous papers we have presented and evaluated different evolutionary algorithms to this game and shown how their behavior scales with size, looking mainly at the game-playing performance. In this paper we fine-tune the parameters of the evolutionary algorithms so that the worst case number of evaluations, and thus the average and median, are improved, resulting in a better solution in a more reliably predictable time.

Cloud computing, in general, is becoming part of the toolset that the scientist uses to perform compute-intensive tasks. In particular, cloud storage is an easy and convenient way of storing files that will be accessible over the Internet, but also a way of distributing those files and performing distributed computation using them. In this paper we describe how such a service commercialized by Dropbox is used for pool-based evolutionary algorithms. A prototype system is described and its peformance measured over a deceptive combinatorial optimization problem, finding that, for some type of problems and using commodity hardware, cloud storage systems can profitably be used as a platform for distributed evolutionary algorithms. Preliminary results show that Dropbox is indeed a viable alternative for execution of pool-based distributed evolutionary algorithms, showing a good scaling behavior with up to 4 computers.

Manual visualization-based sleep stage classification is a time-consuming task prone to errors. Since the correct identification of sleep stages is vital for the correct identification of sleep disorders and for the research in this field in general, there is a growing demand for efficient automatic classification methods. However, there is still no symbolic representation of the biomedical signals that leads to a reliable and accurate automatic sleep classification system. This work presents the application of a novel method for symbolic representation of the EEG and evaluates its potential as information source for a sleep stage classifier, in this case a SVM classifier. The data is first analyzed using Self-Organizing Maps (SOM) and a mutual information (MI)-based variable selection algorithm. Preliminary results of sleep data classification provide success rates around 70%. These results are promising since only EEG is used, and there is still room for improvement in this new symbolic representation of the signal.

This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of the simulated annealing (Sim-Ann) method and the different parameters it requires, such as those related to: the neighbourhood; the cooling scheme; the initial temperature; the number of times the cooling scheme is applied; and the number of times we search for best individual before the temperature is cooled. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameter presenting values that are statistically most significant. Following this idea, the significance and relative importance of the parameters with respect to the obtained results, as well as suitable values for each of these, were obtained using ANOVA on four well known function optimization problems.

In this paper we employ a steady state genetic algorithm to evolve different types of behaviour for bots in the Unreal Tournament 2004™computer game. For this purpose we define three fitness functions which are based on the number of enemies killed, the lifespan of the bot and a combination of both. Long run experiments were carried out, in which the evolved bots' behaviours outperform those of standard bots supplied by the game, particularly in those cases where the fitness involves a measure of the bot's lifespan. Also, there is an increase in the number of items collected, and the behaviours tend to become less aggressive, tending instead towards a more optimised combat style. Further “short run” experiments were carried out with a further type of fitness function defined, based on the number of items picked. In these cases the bots evolve performances towards the goal they have been aimed, with no other behaviours arising, except in the case of the multiple objective one. We conclude that in order to evolve interesting behaviours more complex fitness functions are needed, and not necessarily ones that directly include the goal we are aiming for.

In this work we employ a steady state genetic algorithm to evolve bots' behaviors in the Unreal Tournament 2004 game. Our aim is to show whether interesting behaviors can be obtained with simple fitness functions. For this purpose we define four functions, measuring the number of enemies killed, the bot's lifespan, a combination of both and the number of items collected. The experiments show that incorporating a measure of the bot's lifespan in the fitness results in an optimal behavior in all aspects considered; further, the bots evolved this way outperform the standard bots supplied by the game. In addition, there is an increase in the number of items collected (even when this is not explicitly included in the fitness) and a tendency towards a more optimised combat style with less aggressive behaviors.

The Mastermind puzzle consists in finding out a secret combination by playing others in the same search space and using the hints obtained as a response (which reveal how close the played combination is to the secret one) to produce new combinations and eventually the secret one. Despite having been researched for a number of years, there are still several open issues, such as finding a strategy to select the next combination to play that is able to consistently obtain good results, at any problem size, and also doing it in as little time as possible. In this paper we cast this as a constrained optimization problem, introducing a new fitness function for evolutionary algorithms that takes that fact into account, and compare it to other solutions (exhaustive/heuristic and evolutionary), finding that it is able to obtain the consistently good solutions, and in as little as 30% less time than previously published evolutionary algorithms [2].

This work presents a feasible solution to the problem of book losses prediction from financial and general data in companies. The specific problem tackled in this work corresponds to a real dataset of Spanish companies. A Mutual Information-based criterion has been applied in order to reduce the initial set of variables, and a Support Vector Machine classifier has been designed to perform the prediction. The results show that the proposed approach obtains an important reduction of the number of variables needed to perform the prediction, improving the generalization capabilities of the model. The accuracy rates were above the 84% in the test set, much better than those obtained by other soft-computing algorithms (such as Genetic Programming, Self-Organizing Maps or Artificial Neural Networks) working with the same dataset and presented in previous works. The proposed approach shows to be promising and could be determinant in providing the experts with the right tools for the selection of the relevant factors and for the prediction in this difficult problem.

This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired heuristics. Preliminary results show that some methods are able to attain success rates close to 70% when compared to an expert neurologist. Although still not sufficient to implement a reliable sleep classifier, these are promising results that, together with an analysis via Self-Organizing Maps and ant-based clustering, may help to improve the feature extraction and contribute to a better representation of the different classes' characteristics.

In this paper we compare different policies to select individuals to migrate in an island model. Our thesis is that choosing individuals in a way that exploits differences between populations can enhance diversity, and improve the system performance. This has lead us to propose a family of policies that we call multikulti, in which nodes exchange individuals different "enough" among them. In this paper we present a policy according to which the receiver node chooses the most different individual among the sample received from the sending node. This sample is randomly built but only using individuals with a fitness above a threshold. This threshold is previously established by the receiving node. We have tested our system in two problems previously used in the evaluation of parallel systems, presenting different degree of difficulty. The multikulti policy presented herein has been proved to be more robust than other usual migration policies, such as sending the best or a random individual.

In this paper, we present a multiobjective ant colony optimization (MOACO) algorithm, called CHAC, designed to solve the problem of finding the path for a military unit that minimizes the cost in resources while maximizing safety. Unlike previous MOACO algorithms, CHAC uses a single colony and two different state transition rules: One that combines the heuristic and pheromone information of both objectives and another based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in different scenarios (maps with different degrees of difficulty), outperforming a greedy algorithm (taken as baseline), and yielding a good military behavior in the tactical sense. In comparison, the combined rule is slightly better than the rule based on dominance. © 2009 Wiley Periodicals, Inc.

In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimize both error types in classification: artificial neural networks and function trees ensembles created through multiobjective optimization. Since the multiobjective optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.

In this paper we present the Gossip-based Evolvable Agent Model (GossEvAg) within the context of parallel fine-grained Evolutionary Algorithms (EAs). It extends the Cellular Evolutionary Algorithm (CEA) definition with two novel features designed to work on Peer-to-Peer (P2P) networks: every individual is self-scheduled in a single thread and dynamically self-organizes its neighbourhood via newscasting, a gossip protocol. As a consequence of such multi-threading model, each Evolvable Agent (EvAg) updates asynchronously its state at random depending on the underlying platform scheduler. In order to assess the effects of asynchrony and the gossip protocol, we perform an experimental evaluation of the model for a set of discrete optimization problems. As a baseline for comparison we use two canonical genetic algorithms (GA): A steady-state GA (ssGA) and a generational GA (gGA). We also test two more topologies for the EvAg, a complete graph topology which allows panmixia and a Watts-Strogatz topology which has shown good theoretical and empirical results in related papers. We found that leaving the management of the EvAg to the underlying platform scheduler has an interesting emerging feature: the model is able to scale seamlessly in desktop computers without any effort from the practitioner. We measure how the algorithm speed scales by conducting the experiments in a Single and a Dual-Core Processor architectures.

In a connected world, spare CPU cycles are up for grabs, if you only make its obtention easy enough. In this paper we present a distributed evolutionary computation system that uses the computational capabilities of the ubiquituous web browser. Asynchronous Javascript and JSON (Javascript Object Notation, a serialization protocol) allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in a genetic algorithm experiment with little effort, or none at all. Since, in this case, computing becomes a social activity and is inherently impredictable, in this paper we will explore the performance of this kind of virtual computer by solving simple problems such as the Royal Road function and analyzing how many machines and evaluations it yields. We will also examine possible performance bottlenecks and how to solve them, and, finally, issue some advice on how to set up this kind of experiments to maximize turnout and, thus, performance. The experiments show that we we can obtain high, and to a certain point, reliable performance from volunteer computing based on AJAJ, with speedups of up to several (averaged) machines.

This paper presents a statistical parameter analysis of the ant colony optimization algorithm that was implemented to solve the bi-criteria military path-finding problem. Three parameters have been studied using analysis of variance (ANOVA) in order to identify their influence in the results and the most suitable values for them: number of ants, number of iterations and exploration/exploitation factor. In addition, a mean analysis has been performed in order to complete the conclusions obtained. The study has yielded optimal values for the parameters under study, and some internal relationships between them have been identified.

The design of computer architectures is a very complex problem. The multiple parameters make the number of possible combinations extremely high.Many researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter settings. Instead of exploring the search space, simulating many configurations, our method randomly selects some architecture configurations; those are simulated to obtain their performance, and then an artificial neural network is trained to predict the remaining configurations performance. Results obtained show a high accuracy of the estimations using a simple method to select the configurations we have to simulate to optimize the MLP. In order to explore the search space, we have designed a genetic algorithm that uses the MLP as fitness function to find the niche where the best architecture configurations (those with higher performance) are located. Our models need only a small fraction of the design space, obtaining small errors and reducing required simulation by two orders of magnitude.

This paper introduces a procedure based on genetic programming to evolve XSLT programs (usually called stylesheets or logicsheets). XSLT is a general purpose, document-oriented functional language, generally used to transform XML documents or, in general, solve any problem that can be coded as an XML document. The proposed solution uses a tree representation for the stylesheets as well as diverse specific operators in order to obtain, in the studied cases and a reasonable time, a XSLT stylesheet that performs the transformation.

In this work we compare two soft-computing methods for producing models that are able to predict whether a company is going to have book losses: artificial neural networks (ANNs) and genetic programming (GP). In order to build prediction models that can be applied to an extensive number of practical cases, we need simple models which require a small amount of data. Kohonen's self-organizing map (SOM) is a non-supervised neural network that is usually used as a clustering tool. In our case a SOM has been used to reduce the dimensions of the prediction problem. Traditionally, ANNs have been considered able to produce better classifier structures than GP. In this work we merge the capability of GP for generating classification trees and the feature extraction abilities of SOM, obtaining a classification tool that beats the results yielded using an evolutionary ANN method.

The realization of a control unit can be done using a complex circuitry or microprogramming. The latter may be considered as an alternative method of implementation of machine instructions that can reduce the complexity and increase the flexibility of the control unit. The microcode efficiency and speed are of vital importance for the computer to execute machine instructions fast. This is a difficult task and it requires expert knowledge. It would be interesting and very helpful to have automated tools that, given a machine instruction description, could generate an efficient and correct microprogram. A good option is to use evolutionary computation techniques, which have proved been effective in the evolution of computer programs. In this paper, we intend to show how evolutionary computing techniques could be used to face this problem of generating efficient microprograms. We have developed a microarchitecture simulator of a real machine in order to evaluate an individual and to assign it the fitness value (to determine whether this candidate solution correctly implements the instruction machine). The proposed method is successful in generating correct solutions, not only for the machine code instruction set, but for new machine instructions not included in such set. We have shown that our approach can generate microprogramms to execute (to schedule microinstructions) the machine level instructions for a real machine. Moreover, this evolutive method could be applied to any microarchitecture just by changing the microinstruction set and pre-conditions of each machine instruction to guide evolution.