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Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent(More)
Inductive learning in First-Order Logic (FOL) is a hard task due to both the prohibitive size of the search space and the computational cost of evaluating hypotheses. This paper introduces an evolutionary algorithm for concept learning in (a fragment of) FOL. The algorithm evolves a population of Horn clauses by repeated selection, mutation and optimization(More)
Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in(More)
MOTIVATION The prediction of a protein's contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of(More)
This paper illustrates how external (or social) symbol grounding can be studied in simulations with large populations. We discuss how we can simulate language evolution in a relatively complex environment which has been developed in the context of the New Ties project. This project has the objective of evolving a cultural society and, in doing so, the(More)
The main motivation for using a multi-objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters in gene expression matrix, several objectives have to be optimized simultaneously, and often these objectives are in conflict with each other. Moreover, the use of evolutionary(More)
This paper proposes a method for dealing with numerical attributes in inductive concept learning systems based on genetic algorithms. The method uses constraints for restricting the range of values of the attributes and novel stochastic operators for modifying the constraints. These operators exploit information on the distribution of the values of an(More)
This paper analyzes experimentally discretization algorithms for handling continuous attributes in evolutionary learning. We consider a learning system that induces a set of rules in a fragment of first-order logic (Evolutionary Induc-tive Logic Programming), and introduce a method where a given discretization algorithm is used to generate initial(More)
The NewTies project is implementing a simulation in which societies of agents are expected to develop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are intended to be analogous to those faced by early, simple,(More)