Pedro Ángel Castillo Valdivieso

SA-Prop: Optimization of Multilayer Perceptron Parameters Using Simulated Annealing

A general problem in model selection is to obtain the right parameters that make a model fit observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to find appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train MLPs with a single hidden layer, termed SA-Prop. SimAnn selects the initial weights and the learning rate of the network. SA-Prop combiens the advantages of the stochastic search performed by the Simulated Annealing over the MLP parameter space and the local search of the BP algorithm.

Optimisation of Multilayer Perceptrons Using a Distributed Evolutionary Algorithm with SOAP

SOAP (simple object access protocol) is a protocol that allows the access to remote objects independently of the computer architecture and the language. A client using SOAP can send or receive objects, or access remote object methods. Unlike other remote procedure call methods, like XML-RPC or RMI, SOAP can use many different transport types (for instance, it could be called as a CGI or as sockets). In this paper an approach to evolutionary distributed optimisation of multilayer perceptrons (MLP) using SOAP and language Perl has been done.Obtained results show that the parallel version of the developed programs obtains similar or better results using much less time than the sequential version, obtaining a good speedup. Also it can be shown that obtained results are better than those obtained by other authors using different methods.

Genetic Algorithm Visualization Using Self-organizing Maps

This paper gives an overview of evolutionary computation visualization and describes the application of visualization to some well known multidimensional problems. Self-Organizing Maps (SOM) are used for multidimensional scaling and projection. We show how different ways of training the SOM make it more or less adequate for the visualization task.

Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks

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. Kohonens 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.

Statistical analysis of parameter setting in real-coded evolutionary algorithms

When evolutionary algorithm (EA) applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of the algorithm. This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of an EA method and the different parameters it requires, such as those related to the number of generations, population size, operators application and selection type. 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.

Specifying evolutionary algorithms in XML

The problem of inferring genetic networks under the Temporal Boolean Network model is considered here. This is a very hard problem for which an heuristic approach is proposed. This approach is based on the use of evolutionary algorithms (EAs) to refine the results of a specialized algorithm (ID3). Experimental results provide support for the usefulness of this approach, showing a consistent enhancement of the ID3 solutions.

Evolvable Agents: A Framework for Peer-to-Peer Evolutionary Algorithms

Distributed evolutionary computation programs often needs increasingly big amounts of computational power when tackling large instances of hard optimization problems, and Peer-to-Peer (P2P) systems could be an option for building the large virtual supercomputer in which they could be run. Even as distributed Evolutionary Algorithms (EA) do take advantage of parallel execution by simultaneously promoting diversity and reducing runtime, there are still many challenges on the parallelization of EAs in P2P systems. In this chapter we present a survey of the state of the art in P2P EAs and our solutions to the main P2P issues such as decentralization, massive scalability and fault tolerance.

Using SOM for Neural Network Visualization

Soft ware visualization is an area of computer science deoted to supporting the understanding and effective use of algorithms. The application of soft ware visualization to Evolutionary Conputation has been receiving increasing technique to an evolutionary algorithm for multilayer preceptron training. Our goal is to better understand its internal behavior in order to improve the evolutionary part of the method. The effect of several genetic operators are compare and the difference with a fitness sharing version of the algorithm.

Evolutionary Computation Visualization: Application to G-PROP

Software visualization is an area of computer science devoted to supporting the understanding and effective use of algorithms. The application of software visualization to Evolutionary Computation has been receiving increasing attention during the last few years. In this paper we apply visualization technique to an evolutionary algorithm for multilayer perceptron training. Our goal is to better understand its internal behavior in order to improve the evolutionary part of the method. As a result of applying this this technique several deficiencies in the method have been discovered.

Cybersecurity and honeypots: Experience in a scientific network infrastructure

When dealing with security concerns in the use of network infrastructures a good balance between security concerns and the right to privacy should be maintained. This is very important in scientific networks, because they were created with an open and decentralized philosophy, in favor of the transmission of knowledge, when security was not a essential topic. Although private and scientific information have an enormous value for an attacker, the user privacy for legal and ethical reasons must be respected. Thus, passive detection methods in cybersecurity such as honeypots are a good strategy to achieve this balance between security and privacy in the defense plan of a scientific network. In this paper we present the practical case of the University of Granada in the application of honeypots for the detection and study of intrusions, which avoid intrusive techniques such as the direct analysis of the traffic through networking devices.