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Multi-Objective Biclustering: When Non-dominated Solutions are not Enough

Journal of Mathematical Modelling and Algorithms 2009年第2期8卷 175-202页

作者： Guilherme Palermo Coelho Fabrício Olivetti de França Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing (LBiC) Department of Computer Engineering and Industrial Automation (DCA) School of Electrical and Computer Engineering (FEEC) University of Campinas (Unicamp) Campinas Brazil

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features, and, consequently, to allow the extraction of more accurate information from datasets. Given that biclustering requires the optimization of at least two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, these algorithms only focus their search in the generation of a global set of non-dominated biclusters, which may be insufficient for most of the problems as the coverage of the dataset can be compromised. In order to overcome such problem, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, was proposed. In this work, the MOM-aiNet algorithm will be described in detail, and an extensive set of experimental comparisons will be performed, with the obtained results of MOM-aiNet being confronted with those produced by the popular CC algorithm, by another immune-inspired approach for biclustering (BIC-aiNet), and by the multi-objective approach for biclustering proposed by Mitra & Banka.

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Multi-objective Bayesian Artificial Immune System: Empirical Evaluation and Comparative Analyses

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Journal of Mathematical Modelling and Algorithms 2009年第2期8卷 151-173页

作者： Castro, Pablo A. D. Von Zuben, Fernando J. Laboratory of Bioinformatics and Bioinspired Computing - LBiC Department of Computer Engineering and Industrial Automation - DCA School of Electrical and Computer Engineering - FEEC University of Campinas - UNICAMP Campinas Brazil

Recently, we have proposed a Multi-Objective Bayesian Artificial Immune System (MOBAIS) to deal effectively with building blocks (high-quality partial solutions coded in the solution vector) in combinatorial multi-objective problems. By replacing the mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions, MOBAIS takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions. The preliminary results have indicated that our proposal is able to properly build the Pareto front. Motivated by this scenario, this paper better formalizes the proposal and investigates its usefulness on more challenging problems. In addition, an important enhancement regarding the Bayesian network learning was incorporated into the algorithm in order to speed up its execution. To conclude, we compare MOBAIS with state-of-the-art algorithms taking into account quantitative aspects of the Pareto front found by the algorithms. MOBAIS outperforms the contenders in terms of the quality of the obtained solutions and requires an amount of computational resource inferior or compatible with the contenders.

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A Complex Neighborhood based Particle Swarm Optimization

A Complex Neighborhood based Particle Swarm Optimization

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Congress on Evolutionary Computation

作者： Alan Godoy Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing (LBiC) School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Sao Paulo Brazil

This paper proposes a new variant of the PSO algorithm named complex neighborhood particle swarm optimizer (CNPSO) for solving global optimization problems. In the CNPSO, the neighborhood of the particles is organized through a complex network which is modified during the search process. This evolution of the topology seeks to improve the influence of the most successful particles and it is fine tuned for maintaining the scale-free characteristics of the network while the optimization is being performed. The use of a scale-free topology instead of the usual regular or global neighborhoods is intended to bring to the search procedure a better capability of exploring promising regions without a premature convergence, which would result in the procedure being easily trapped in a local optimum. The performance of the CNPSO is compared with the standard PSO on some well-known and high-dimensional benchmark functions, ranging from multimodal to plateau-like problems. In all the cases the CNPSO outperformed the standard PSO.

关键词： Particle swarm optimization Complex networks Network topology Evolution (biology) bioinformatics Computer networks Convergence Biological system modeling Humans Collaborative tools

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A dynamic artificial immune algorithm applied to challenging benchmarking problems

A dynamic artificial immune algorithm applied to challenging...

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Congress on Evolutionary Computation

作者： Fabricio Olivetti de Franca Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing (LBiC) Department of Computer Engineering and Industrial Automation (DCA) School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Sao Paulo Brazil

In many real-world scenarios, in contrast to standard benchmark optimization problems, we may face some uncertainties regarding the objective function. One source of these uncertainties is a constantly changing environment in which the optima change their location over time. New heuristics or adaptations to already available algorithms must be conceived in order to deal with such problems. Among the desirable features that a search strategy should exhibit to deal with dynamic optimization are diversity maintenance, a memory of past solutions, and a multipopulation structure of candidate solutions. In this paper, an immune-inspired algorithm that presents these features, called dopt-aiNet, is properly adapted to deal with six newly proposed benchmark instances, and the obtained results are outlined according to the available specifications for the competition at the Congress on Evolutionary Computation 2009.

关键词： Heuristic algorithms Immune system Uncertainty Evolutionary computation Space exploration Artificial immune systems Robustness Routing Finite impulse response filter Blind equalizers

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Improving a multi-objective multipopulation artificial immune network for biclustering

Improving a multi-objective multipopulation artificial immun...

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Congress on Evolutionary Computation

作者： Guilherme Palermo Coelho Fabricio Olivetti de Franca Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing State University of Campinas-UNICAMP Sao Paulo Brazil Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil School of Electrical and Computer Engineering State University of Campinas-UNICAMP Sao Paulo Brazil

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques. Given that biclustering requires the optimization of at least two conflicting objectives and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, apart from the individual characteristics of the biclusters that should be optimized during their construction, several other global aspects should also be considered, such as the coverage of the dataset and the overlap among biclusters. These requirements will be addressed in this work with the MOM-aiNet+ algorithm, which is an improvement of the original multi-objective multipopulation artificial immune network denoted MOM-aiNet. Here, the MOM-aiNet+ algorithm will be described in detail, its main differences from the original MOM-aiNet will be highlighted, and both algorithms will be compared, together with three other proposals from the literature.

关键词： Proposals Data mining Clustering algorithms Standards development Evolutionary computation Information analysis Gene expression Collaboration Information filtering Information filters

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A Multiobjective Approach to Phylogenetic Trees: Selecting the Most Promising Solutions from the Pareto Front

A Multiobjective Approach to Phylogenetic Trees: Selecting t...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： G.P. Coelho F.J. Von Zuben A.E.A. da Silva Univ. of Campinas - Unicamp Campinas Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Brazil School of Mathematical and Nature Sciences Methodist University of Piracicaba Brazil

This work presents the application of the omni-aiNet algorithm - an immune-inspired algorithm originally developed to solve single and multiobjective optimization problems - to the reconstruction of phylogenetic trees. The main goal of this work is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time the minimal evolution and the mean-squared error criteria. The obtained set of phylogenetic trees contains non-dominated individuals that form the Pareto front and that represent the trade-off of the two conflicting objectives. Given this set of phylogenetic trees, two multicriterion decision-making techniques were applied in order to try to select the best solution within the Pareto front.

关键词： Phylogeny Decision making Proposals Application software Design engineering Organisms DNA Sequences Intelligent systems Laboratories

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Evolutionary Modeling of Larval Dispersal in Blowflies Using Non-Uniform Cellular Automata

Evolutionary Modeling of Larval Dispersal in Blowflies Using...

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Congress on Evolutionary Computation

作者： A.L.T. Romano L. Gomes G. Gomes W. Puma-Villanueva M. Zanetti C.J. von Zuben F.J. von Zuben Laboratory of Bioinformatics and Bioinspired Computing (LBiC) University of Campinas (Unicamp) Campinas SP Brazil Department of Zoology Sao Paulo State University (UNESP) Rio Claro SP Brazil

When the food supply finishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process known as post-feeding larval dispersal. Based on experimental data of the initial and final configuration of the dispersion, the reproduction of such spatio-temporal behavior is achieved here by means of the evolutionary search for cellular automata with a distinct transition rule associated with each cell, also known as a nonuniform cellular automata, and with two states per cell in the lattice. Two-dimensional regular lattices and multivalued states will be considered and a practical question is the necessity of discovering a proper set of transition rules. Given that the number of rules is related to the number of cells in the lattice, the search space is very large and an evolution strategy is then considered to optimize the parameters of the transition rules, with two transition rules per cell. As the parameters to be optimized admit a physical interpretation, the obtained computational model can be analyzed to raise some hypothetical explanation of the observed spatio-temporal behavior.

关键词： Lattices Biological system modeling Computational modeling Mathematical model Physics computing Spatiotemporal phenomena Application software Computer science Quantization Extraterrestrial phenomena

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The Influence of the Pool of Candidates on the Performance of Selection and Combination Techniques in Ensembles

The Influence of the Pool of Candidates on the Performance o...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.P. Coelho F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil

In this paper, we propose the use of an immune-inspired approach called opt-aiNet to generate a diverse set of high-performance candidates to compose an ensemble of neural network classifiers. Being a population-based search algorithm, the opt-aiNet is capable of maintaining diversity and finding many high-performance solutions simultaneously, which are known to be desired features when synthesizing an ensemble. Concerning the selection and combination phases, the most relevant selection and combination techniques already proposed in the literature have been considered. The main contribution of this paper is the indication that there is no pair of selection/combination technique that can be considered the best one, because the performance of the obtained ensemble varies significantly with the current composition of the pool of candidates already produced by the generation phase. Notwithstanding, this variability in performance is not restricted to the choice of opt-aiNet as the generative device. As a consequence, to overcome the performance of the best individual classifier, every possible pairs of selection and combination techniques should be tried. Only with such an exhaustive search (notice that the main computational burden is usually related to the generation phase), the performance of the ensemble was invariably superior to the performance of the best individual classifier on four benchmark classification problems.

关键词： Industrial training Intelligent networks Neural networks Network synthesis Supervised learning Statistical distributions Laboratories bioinformatics Computer industry Automation

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Bayesian Learning of Neural Networks by Means of Artificial Immune Systems

Bayesian Learning of Neural Networks by Means of Artificial ...

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International Joint Conference on Neural Networks (IJCNN)

作者： P.A.D. Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering Faculdade de Engenharia Elétrica e de Computaçãio Sao Paulo Brazil

Once the design of artificial neural networks (ANN) may require the optimization of numerical and structural parameters, bio-inspired algorithms have been successfully applied to accomplish this task, since they are population-based search strategies capable of dealing successfully with complex and large search spaces, avoiding local minima. In this paper, we propose the use of an artificial immune system for learning feedforward ANN's topologies. Besides the number of neurons in the hidden layer, the algorithm also optimizes the type of activation function for each node. The use of a Bayesian framework to infer the weights and weight decay terms as well as to perform model selection allows us to find neural models with high generalization capability and low complexity, once the Occam's razor principle is incorporated into the framework. We demonstrate the applicability of the proposal on seven classification problems and promising results were obtained.

关键词： Bayesian methods Artificial neural networks Artificial immune systems Neurons Immune system Algorithm design and analysis Network topology Design optimization Structural engineering Proposals

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A Supervised Constructive Neuro-Immune Network for Pattern Classification

A Supervised Constructive Neuro-Immune Network for Pattern C...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Knidel L. Nunes de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.

关键词： Pattern classification Immune system Neurons Supervised learning Vector quantization Classification algorithms Prototypes Clustering algorithms Unsupervised learning Artificial neural networks

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