Showing papers by "Leandro Nunes de Castro published in 2003"

An immune learning classifier network for autonomous navigation

Abstract: This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.

Copt-aiNet and the Gene Ordering Problem.

Abstract: Automatic contamination detection remains a problem for EST sequencing projects. Generally this is dealt in an ad-hoc fashion, using similarity as the basic strategy. Here we present the first step towards a more rational methodology. It seeks to extract information from training and target sequences, and to use that information to discriminate legitimate from contaminant sequences. We use six different programs to analyze sequences, and combine their scores in two different ways. We present results obtained from simulations. Measured in terms of sum of false positive and false negative counts, our results show a best performance of 0.5% and a worst performance of 36%

Bioinformatics Data Analysis Using an Artificial Immune Network

Abstract: This work describes a new proposal for gene expression data clustering based on a combination of an immune network, named aiNet, and the minimal spanning tree (MST). The aiNet is an AIS inspired by the immune network theory. Its main role is to perform data compression and to identify portions of the input space representative of a given data set. The output of aiNet is a set of antibodies that represent the data set in a simplified way. The MST is then built on this network, and clusters are determined by using a new method for detecting the inconsistent edges of the tree. An important advantage of this technique over the classical approaches, like hierarchical clustering, is that there is no need of previous knowledge about the number of clusters and their distributions. The hybrid algorithm was first applied to a benchmark data set to demonstrate its validity, and its results were compared with those produced by other approaches from the literature. Using the full yeast S. cerevisiae gene expression data set, it was possible to detect a strong interconnection of the genes, hindering the perception of inconsistencies that may lead to the separation of data into clusters.

Blind search for optimal Wiener equalizers using an artificial immune network model

Abstract: This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.