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Heuristics to avoid redundant solutions on population-based multimodal continuous optimization

Heuristics to avoid redundant solutions on population-based ...

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Congress on Evolutionary Computation

作者： Rodrigo Pasti Fernando J. Von Zuben Renato Dourado Maia Leandro Nunes de Castro Laboratory of Bioinformatics and Bio-inspired Computing (LBiC) School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Campinas Brazil Department of Computer Science State University of Montes Claros Montes Claros Brazil Natural Computing Laboratory (LCoN) Faculty of Electrical Engineering and Computer Science Mackenzie University Sao Paulo Brazil

In population-based meta-heuristics, the generation and maintenance of diversity seem to be crucial to deal with multimodal continuous optimization. However, usually this crucial aspect is not an inherent feature of generally adopted meta-heuristics. In this paper, we propose to associate diversity maintenance with the detection and elimination of redundant candidate solutions in the search space, more specifically candidate solutions located at the same attraction basin of a local optimum. Two low computational cost heuristics are proposed to detect redundancy, in a pairwise comparison of candidate solutions and by extracting local features of the fitness landscape at runtime. Those heuristics are not tied to a specific class of algorithms, and are thus able to be incorporated into a broad range of population-based meta-heuristics, and even into multiple executions of non-population-based algorithms. In a set of experimental results, the two heuristics were implemented as an attached module of an already existing multipopulation meta heuristics, and the results indicate that they operate properly, no matter the number and conformation of the attraction basins in multimodal optimization problems.

关键词： Optimization Redundancy Heuristic algorithms Minimization Computational efficiency Maintenance engineering Systematics

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Analysing learning classifier systems in reactive and non-reactive robotic tasks

引用

11th International Workshops on Learning Classifier Systems, WLCS 2007

作者： Moioli, Renan C. Vargas, Patricia A. Von Zuben, Fernando J. Laboratory of Bioinformatics and Bio-Inspired Computing - LBiC School of Electrical and Computer Engineer - FEEC/Unicamp Campinas-SP Brazil Department of Informatics University of Sussex Falmer Brighton BN1 9QH United Kingdom

ISBN: (纸本)3540881379

There are few contributions to robot autonomous navigation applying Learning Classifier Systems (LCS) to date. The primary objective of this work is to analyse the performance of the strength-based LCS and the accuracy-based LCS, named EXtended Learning Classifier System (XCS), when applied to two distinct robotic tasks. The first task is purely reactive, which means that the action to be performed can rely only on the current status of the sensors. The second one is non-reactive, which means that the robot might use some kind of memory to be able to deal with aliasing states. This work presents a rule evolution analysis, giving examples of evolved populations and their peculiarities for both systems. A review of LCS derivatives in robotics is provided together with a discussion of the main findings and an outline of future investigations. © 2008 Springer Berlin Heidelberg.

关键词： Robots

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Towards the evolution of an artificial homeostatic system

Towards the evolution of an artificial homeostatic system

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Congress on Evolutionary Computation

作者： Renan C. Moioli Patricia A. Vargas Fernando J. Von Zuben Phil Husbands Laboratory of Bioinformatics and Bio-Inspired Computing LBiC-School of Electrical and Computer Engineering Sao Paulo Brazil Centre for Computational Neuroscience and Robotics (CCNR) Department of Informatics University of Sussex Brighton UK

This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. The switching among the NSGasNets is implemented as an artificial endocrine system (AES), which is based on a system of coupled nonlinear difference equations. The NSGasNets are synthesized by means of an evolutionary algorithm. The obtained neuro-endocrine controller is adopted in simulated and real benchmark applications, and the additional flexibility provided by the use of NSGasNet, together with the existence of an automatic coordination system, guides to convincing levels of performance.

关键词： biochemistry Robots Neurons Sensors Distance measurement Robot sensing systems Robot kinematics

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Evaluating the performance of a biclustering algorithm applied to collaborative filtering - A comparative analysis

Evaluating the performance of a biclustering algorithm appli...

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7th International Conference on Hybrid Intelligent Systems, HIS 2007

作者： De Castro, Pablo A. D. De França, Fabrício O. Ferreira, Hamilton M. Von Zuben, Fernando J. Laboratory of Bioinformatics and Bio-Inspired Computing -LBIC School of Electrical and Computer Engineering - FEEC University of Campinas - UNICAMP Campinas-SP Brazil

ISBN: (纸本)0769529461

Collaborative filtering (CF) is a method to perform automated suggestions for a user based on the opinion of other users with similar interest. Most of the CF algorithms do not take into account the existent duality between users and items, considering only the similarities between users or only the similarities between items. The authors have proposed in a previous work a bio-inspired methodology for CF, namely BIC-aiNet, capable of clustering rows and columns of a data matrix simultaneously. The usefulness and performance of the methodology are reported in the literature. Now, the authors carry out more rigorous comparative experiments with BIC-aiNet and other techniques found in the literature, as well as evaluate the scalability of the algorithm in several datasets of different sizes. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF. © 2007 IEEE.

关键词： Collaborative filtering

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A Wrapper for Projection Pursuit Learning

A Wrapper for Projection Pursuit Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Leonardo M. Holschuh Clodoaldo A. M. Lima Fernando J. Von Zuben Laboratory of Bioinformatics and Bio-inspired Computing (LBiC) State University of Campinas-UNICAMP Sao Paulo Brazil

Constructive algorithms have shown to be reliable and effective methods for designing artificial neural networks (ANN) with good accuracy and generalization capability, yet with parsimonious network structures. Projection pursuit learning (PPL) has demonstrated great flexibility and effectiveness in performing this task, though presenting some difficulties in the search for appropriate projection directions in input spaces with high dimensionality. Due to the existence of high-dimensional input spaces in the context of time series prediction, mainly under the existence of long-term dependencies in the time series, we propose here a method based on the wrapper methodology to perform variable selection, so that only a subset of highly-informative lags is going to be considered as the regression vector. The yearly sunspot number time series is adopted as a case study and comparative analysis is performed considering alternative approaches in the literature, guiding to competitive results.

关键词： Neurons Pursuit algorithms Artificial neural networks Input variables Time series analysis Neural networks Laboratories bioinformatics Design methodology Algorithm design and analysis

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Evaluating the Performance of a Biclustering Algorithm Applied to Collaborative Filtering - A Comparative Analysis

Evaluating the Performance of a Biclustering Algorithm Appli...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Pablo A. D. de Castro Fabricio O. de Franca Hamilton M. Ferreira Fernando J. Von Zuben Laboratory of Bioinformatics and Bio-Inspired Computing-LBIC School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil

关键词： Filtering algorithms Performance analysis Algorithm design and analysis Clustering algorithms Information filtering Information filters International collaboration Scalability Hybrid intelligent systems Laboratories

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Applying Biclustering to Perform Collaborative Filtering

Applying Biclustering to Perform Collaborative Filtering

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Pablo A.D. de Castro Fabricio O. de Franca Hamilton M. Ferreira Fernando J. Von Zuben Laboratory of Bioinformatics and Bio-Inspired Computing-LBIC UNICAMP Campinas-SP Sao Paulo Brazil Universidade Estadual de Campinas Campinas SP BR

Collaborative filtering (CF) is a method to perform automated suggestions for a user based on the opinion of other users with similar interest. Most of the CF algorithms do not take into account the existent duality between users and items, considering only the similarities between users or only the similarities between items. In this paper we propose a novel methodology for the CF capable of dealing with this situation. By proposing an immune-inspired bi clustering technique to carry out clustering of rows and columns at the same time, our algorithm is able to group similarities between users and items. In order to evaluate the proposed methodology, we have applied it to Movie Lens dataset which contains user's ratings to a large set of movies. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF reported in the literature.

关键词： Filtering Clustering algorithms Motion pictures International collaboration Intelligent systems Application software Laboratories bioinformatics Design engineering Immune system

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Immune Learning Classifier Networks: Evolving Nodes and Connections

Immune Learning Classifier Networks: Evolving Nodes and Conn...

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Congress on Evolutionary Computation

作者： R.R. Cazangi F.J. von Zuben Laboratory of Bioinformatics and Bio-Inspired Computing LBiC DCA-FEEC State University of Campinas-UNICAMP Brazil

The design of an autonomous navigation system with multiple tasks to be accomplished in unknown environments represents a complex undertaking. With the simultaneous purposes of capturing targets and avoiding obstacles, the challenge may become still more intricate if the configuration of obstacles and targets creates local minima, like concave shapes and mazes between the robot and the target. Pure reactive navigation systems are not able to deal properly with such hampering scenarios, requiring additional cognitive apparatus. Concepts from immune network theory are then employed to convert an earlier reactive robot controller, based on learning classifier systems, into a connectionist device. Starting from no a priori knowledge, both the classifiers and their connections are evolved during the robot navigation. Some experiments with and without local minima are carried out and the proposed evolutionary network of classifiers was shown to produce connectionist navigation systems capable of successfully overcoming local minima.

关键词： Navigation Cognitive robotics Robot sensing systems Immune system Shape Control systems Associate members bioinformatics Robot control Mobile robots

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An Immunological Density-Preserving Approach to the Synthesis of RBF Neural Networks for classification

An Immunological Density-Preserving Approach to the Synthesi...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T.V. Barra G.B. Bezerra L.N. de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC) Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil

Radial basis function (RBF) neural networks are universal approximators and have been used for a wide range of applications. Aiming at reducing the number of neurons in the hidden layer, for regularization purposes, the center and dispersion of each RBF have to be properly defined by means of competitive learning. Only the output weights will be defined in a supervised manner. One of the drawbacks of such learning methodology, involving unsupervised and supervised learning, is that the centers will be defined so that regions in the input space with a high density of samples tend to be under-represented and those regions with a low density of samples tend to be over-represented. Additionally, few approaches provide a proper and individual indication of the dispersion of each RBF. This paper presents an immune density-preserving algorithm with adaptive radius, called ARIA, to determine the number of centers, their location and the dispersion of each RBF, based only on the available training data set. Considering classification problems, the algorithm to determine the hidden layer is compared to another immune-inspired algorithm called aiNet, K-means and the random choice of centers. The classification accuracy of the final network is compared to another density based approach and a decision tree classifier, C 5.0. The results are reported and analyzed.

关键词： Network synthesis Neural networks Neurons Radial basis function networks Prototypes Classification tree analysis Industrial training Computer networks Supervised learning Training data

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Evolutionary Stigmergy in Multipurpose Navigation Systems

Evolutionary Stigmergy in Multipurpose Navigation Systems

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Congress on Evolutionary Computation

作者： R.R. Cazangi F.J. von Zuben M.F. Figueiredo Laboratory of Bioinformatics and Bio-Inspired Computing (LBiC) DCA-FEEC State University of Campinas-UNICAMP Brazil DIN State University of Maringa Brazil

Autonomous robot navigation involves many challenges and difficulties which are augmented when multiple robots operate together. Sophisticated computational techniques are required to cope with autonomous navigation in collective robotics, being the biologically-inspired approaches the most frequently adopted. Stigmergy, i.e. the ants communication by means of pheromones, is the main biological metaphor used in this work to perform multi-robot communication. The robots will be able to mark regions of the environment with artificial pheromones, according to past experiences, assisting one another in a cooperative and indirect way to accomplish the navigation objectives. Each robot is controlled by an autonomous navigation system (ANS) based on learning classifier system, which evolves during navigation from no a priori knowledge. Besides learning to avoid obstacles and capture targets, the systems must also learn how and where to lay artificial pheromones. Some experiments and simulations are performed intending to particularly investigate the ANS from three main perspectives: capability of learning to achieve the navigation objectives in collective scenarios, adaptability in face of environmental changes and ability to obtain optimized navigation behaviors by means of stigmergy.

关键词： Navigation Robot sensing systems biological information theory Insects Laboratories bioinformatics biology computing Robot control Communication system control Control systems

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