Anna Esparcia-Alcázar

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We propose a novel design paradigm for recurrent neural networks. This employs a two-stage Genetic Programming / Simulated Annealing hybrid algorithm to produce a neural network which satisfies a set of design constraints. The Genetic Programming part of the algorithm is used to evolve the general topology of the network, along with specifications for the(More)
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(More)
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(More)
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(More)
In P2P and volunteer computing environments, resources are not always available from the beginning to the end, getting incorporated into the experiment at any moment. Determining the best way of using these resources so that the exploration/exploitation balance is kept and used to its best effect is an important issue. The Intermediate Disturbance(More)
In this paper we present the application of a genetic programming algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database of Spanish companies. The database has two important drawbacks: the number of bankrupt companies is very small when compared with the number of healthy ones (unbalanced data) and a(More)
In this paper we employ a steady state genetic algorithm to evolve different types of behaviour for bots in the Unreal Tournament 2004TMcomputer 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(More)
The development of Peer-to-Peer (P2P) systems is still a challenge due to the huge number of factors involved. Validation of these systems must be defined in terms of describing the adequacy of the P2P model to the actual environment. This paper focuses on the validation of the Distributed Resource Machine (DRM) as a computational P2P system when applied to(More)
This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new.(More)