Basic Algorithms and Operators
Basic Algorithms and Operators
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Shifting and scaling patterns from gene expression data
Bioinformatics
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
A multi-objective approach to discover biclusters in microarray data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Practical Approach to Microarray Data Analysis
A Practical Approach to Microarray Data Analysis
A general noise model and its effects on evolution strategy performance
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper we propose a novel representation scheme, called probabilistic encoding. In this representation, each gene of an individual represents the probability that a certain trait of a given problem has to belong to the solution. This allows to deal with uncertainty that can be present in an optimization problem, and grant more exploration capability to an evolutionary algorithm. With this encoding, the search is not restricted to points of the search space. Instead, whole regions are searched, with the aim of individuating a promising region, i.e., a region that contains the optimal solution. This implies that a strategy for searching the individuated region has to be adopted. In this paper we incorporate the probabilistic encoding into a multi-objective and multi-modal evolutionary algorithm. The algorithm returns a promising region, which is then searched by using simulated annealing. We apply our proposal to the problem of discovering biclusters in microarray data. Results confirm the validity of our proposal.