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Congress on Evolutionary Computation

作者： Guilherme P. Coelho Fabrício O. de França Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing (LBiC/DCA) School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Sao Paulo Brazil

Diversity maintenance is an important aspect in population-based metaheuristics for optimization, as it tends to allow a better exploration of the search space, thus reducing the susceptibility to local optima in multimodal optimization problems. In this context, metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though generally implementing very simple mechanisms to control the dynamics of the network. To increase such diversity maintenance capability even further, a new immune-inspired algorithm was recently proposed, which adopted a novel concentration-based model of immune network. This new algorithm, named cob-aiNet (Concentration-based Artificial Immune Network), was originally developed to solve real-parameter single-objective optimization problems, and it was later extended (with cob-aiNet[MO]) to deal with real-parameter multi-objective optimization. Given that both cob-aiNet and cob-aiNet[MO] obtained competitive results when compared to state-of-the-art algorithms for continuous optimization and also presented significantly improved diversity maintenance mechanisms, in this work the same concentration-based paradigm was further explored, in an extension of such algorithms to deal with single-objective combinatorial optimization problems. This new algorithm, named cob-aiNet[C], was evaluated here in a series of experiments based on four Traveling Salesman Problems (TSPs), in which it was verified not only the diversity maintenance capabilities of the algorithm, but also its overall optimization performance.

关键词： Optimization Cloning Heuristic algorithms Immune system Search problems Maintenance engineering Cities and towns

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Assessing the performance of a swarm-based biclustering technique for data imputation

Assessing the performance of a swarm-based biclustering tech...

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Congress on Evolutionary Computation

作者： Rosana Veroneze Fabrício O. de França Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing (LBiC) Department of Computer Engineering and Industrial Automation (DCA) State University of Campinas-UNICAMP Sao Paulo Brazil

Although the missing data problem has been studied for many years, it is still a relevant and challenging problem nowadays. Data can be missing for a variety of reasons, and there are several techniques capable of processing missing data. A parcel of them tries to estimate the missing values. This technique is called imputation. Recently, it was proposed a biclustering algorithm, based on Swarm Intelligence, named SwarmBCluster, to impute missing data. As it is a novel and promising algorithm, this paper intends to investigate the influence of its parameters on the performance. To achieve this objective, this paper will compare SwarmBCluster with other two imputation algorithms and, after that, it will perform a sensitivity analysis. The quality of the imputations is measured with the Root Mean Squared Error (RMSE). The experiments showed that SwarmBCluster presents good results concerning the RMSE metric and that the proper choice of parameters can considerably improve the performance of the algorithm.

关键词： Algorithm design and analysis Data models Measurement Coherence Databases Mathematical model Filtering

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A neural architecture to address Reinforcement Learning problems

A neural architecture to address Reinforcement Learning prob...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rodrigo L. S. de Arruda 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 Campinas Sao Paulo Brazil

In this paper, the Reinforcement Learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel Adaptive Dynamic Programming algorithm which is based on a Multilayer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm.

关键词： Mathematical model Equations Monte Carlo methods Heuristic algorithms Markov processes Approximation methods Dynamic programming

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Extracting additive and multiplicative coherent biclusters with swarm intelligence

Extracting additive and multiplicative coherent biclusters w...

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Congress on Evolutionary Computation

作者： Fabrício O. 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) State University of Campinas-UNICAMP Sao Paulo Brazil

Biclustering is usually referred to as the process of finding subsets of rows and columns from a given dataset expressing a relationship. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other, that may lead to novel discoveries regarding the objects in the dataset. This coherence leads to the possibility of obtaining representative values for rows (subset of objects) and columns (subset of attributes) of each bicluster. In the literature, it is usually studied the additive coherence among elements, i.e. each element is represented by the sum of its respective representative values. But in a given dataset, it is also possible to find multiplicative relations, i.e. each element being represented by the multiplication of its respective representative values, and that may reveal distinct knowledge contained in the objects of the dataset. So, in this paper, a swarm based approach, named SwarmBcluster, is adapted to find both additive and multiplicative coherent biclusters from a dataset, in an attempt to enrich the amount of information provided by the biclusters. Experiments are performed considering two well known datasets and it is found that the multiplicative coherence biclusters improve the quality of the data analysis and may contribute to reduce the influence of noise.

关键词： Coherence Additives Data mining Humans Data analysis Indexes

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Evolutionary multi-objective optimization for the vendor-managed inventory routing problem

Evolutionary multi-objective optimization for the vendor-man...

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Congress on Evolutionary Computation

作者： Regina M. Azuma Guilherme P. Coelho 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 Research and Development Department Motorola Mobility Inc. USA

The class of inventory routing problems (IRPs) is present in several areas, including automotive industry and cash management for ATM networks. In the specific case of vendor-managed IRPs, in which the supplier is responsible for managing the product inventory in each client and for properly providing replenishments, the challenge is to determine which retailers should be served, the amount of product that should be delivered to each of these retailers, and which routes the distribution vehicles should follow, so that the associated costs are minimized. Although this is clearly a multi-objective optimization problem, in the literature it has been generally modeled as a single-objective problem, which limits the scope of the obtained results. Therefore, this work presents a multi-objective approach to solve one version of the IRP usually found in the scientific literature, by simultaneously minimizing both the inventory and transportation costs. The method proposed in this work is based on the well-known SPEA2 (Strength Pareto Evolutionary Algorithm) and includes innovative aspects mainly associated with the representation of candidate solutions, genetic operators and local search. The experiments were performed on a set of known benchmark IRPs from the literature, so that the obtained results could be properly compared to the best solution found for the single-objective version of each problem.

关键词： Planning Vehicles Routing Optimization Proposals Genetics

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On the diversity mechanisms of opt-aiNet: A comparative study with fitness sharing

On the diversity mechanisms of opt-aiNet: A comparative stud...

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Congress on Evolutionary Computation

作者： Fabrício O de França Guilherme P. Coelho 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

Immune-inspired algorithms based on the Immune Network theory have been frequently claimed to be capable of maintaining diversity among the candidate solutions in their population. However, no specific study on this aspect to verify how the intrinsic diversity mechanisms of such immune algorithms behave, when compared to other approaches from the literature, has been made yet. Therefore, in this work we have addressed this issue, by taking the opt-aiNet algorithm (a popular immune-inspired algorithm developed for real-parameter optimization) and comparing its results with those of a modified version, in which the mechanisms associated with diversity maintenance were replaced by a traditional fitness sharing approach. Besides, two distance metrics were also considered for both algorithms: the traditional Euclidean distance, and the Line Distance, a metric proposed in the literature as capable of identifying whether two solutions belong to distinct local optima. The experiments were performed on six benchmark problems from the literature, each of them with distinct characteristics, and the results have shown that the original immune-inspired mechanisms of opt-aiNet are indeed more capable of stimulating the diversity of solutions, and also requiring a smaller amount of computational resources.

关键词： Immune system Euclidean distance Cloning Benchmark testing Lead Optimization

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

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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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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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