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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Godoy, Alan Von Zuben, Fernando J. Laboratory of Bioinformatics and Bioinspired Computing School of Electrical and Computer Engineering University of Campinas Av. Albert Einstein 400 Campinas SP Brazil

ISBN: (纸本)9781450319645

In this work we analyzed the role social networks play in the efficiency of collective problem-solving, evaluating whether the topological characteristics seen in real-world networks yield any performance improvement in such processes. To study this we used the Particle Swarm Optimization as a testbed for social groups performing a collective task, defin- ing the structure of communication between individuals in the swarm through topologies generated by a model for the creation and evolution of social networks. The experimental results indicate that groups using these networks may, in- deed, experience better performance in collective problem- solving, so that these groups were able to overcome the re- sults achieved by swarms using classical neighborhoods for PSO and reached results very close to those found by swarms using the topology of DMS-PSO, usually considered to be part of the state-of-the-art of Particle Swarm Optimization.

关键词： Complex networks

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Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems

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Advances in Software Engineering 2012年第1期2012卷

作者： Gabriel A. Archanjo Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing School of Electrical and Computer Engineering University of Campinas (Unicamp) 13083-970 Campinas SP Brazilunicamp.br

Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks.

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A Concentration-based Artificial Immune Network for combinatorial optimization

A Concentration-based Artificial Immune Network for combinat...

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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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Finding a high coverage set of 5-biclusters with swarm intelligence

Finding a high coverage set of 5-biclusters with swarm intel...

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Congress on Evolutionary Computation

作者： Fabrício O. de França Fernando J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering 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. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other. In order to find such structures, the δ-biclustering problem was formulated, being denoted as the problem of finding a set of biclusters limited by a maximum degree of coherence, measured by a mean-squared residue, while maximizing the bicluster total size. Additionally, it is expected a reduced overlap among the biclusters in the set, in other words, a minimization of the number of common elements shared by them. This also leads to a high coverage of the original dataset given the number of biclusters found. Most algorithms intended to find such biclusters focus only on the mean-squared residue and/or the bicluster size. This usually leads to a set of biclusters that do not fully cover the whole data and, as a consequence, shares a high overlap among them. This may generate redundant information on some portions of the dataset and lack of information on other portions. Also, some methods introduce noise into the dataset in order to promote a better coverage, but sometimes misleading the search. In this paper, a swarm-based approach, named SwarmBcluster, is created to effectively find biclusters without introducing noise and with the main objective of achieving maximum coverage. Experiments were performed considering two well-known datasets and a comparative analysis considering other approaches indicates that SwarmBcluster is capable of finding a set of biclusters with high coverage, while maintaining a high average volume and also obeying the coherence constraint imposed.

关键词： Search problems Particle swarm optimization Coherence Indexes Noise Data mining Ant colony optimization

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A Concentration-based Artificial Immune Network for continuous optimization

A Concentration-based Artificial Immune Network for continuo...

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Congress on Evolutionary Computation

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

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 they generally implement very simple mechanisms to control the dynamics of the network. In the AIS literature, several studies propose different models that try to explain the behavior of immune networks, which are generally based on the concentration of antibodies and tend to better mimic some aspects of such complex systems. Therefore, in this work we propose a novel immune-inspired algorithm for optimization, named cob-aiNet (Concentration-based Artificial Immune Network), that intends to explore such network models and introduce new mechanisms to better control the dynamics of the network, so that a broader coverage of promising regions of the search space can be achieved. This property of cob-aiNet was verified in experimental analyses, in which the algorithm was compared to two other AIS proposals and also to all the competitors from the 2005 CEC Special Session on RealParameter Optimization.

关键词： Cloning Immune system Heuristic algorithms Optimization Search problems Measurement Benchmark testing

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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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