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 g...
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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.
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 accurac...
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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 ...
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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.
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 b...
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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. Projec...
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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.
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 b...
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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.
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 b...
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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.
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...
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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.
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, t...
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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.
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 i...
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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.
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