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This paper applies a heterogeneous parallel ecology-inspired algorithm (pECO) to solve a complex problem from bioinformatics. The ecological-inspired algorithm represents a new perspective to develop cooperative evolutionary algorithms. Different algorithms are applied to compose the computational ecosystem in a heterogeneous model. The aim is to search low energy conformations for the Protein Structure...
There is no doubt that the World Wide Web has made easier the task of searching for information on the Internet. The amount of information obtained (some of them irrelevant ones) increases day after day and creates opportunities for a new breed of systems named "Recommender Systems". These systems have emerged as one successful approach to tackle the problem of information overload. Traditional...
This paper contains a description of an argumentation system that uses a defeasible reasoning mechanism. The main idea and the key points are given. Also it contains main algorithms for detecting the conflicts and finding statuses of arguments. Solutions of some problems, which are not solvable in the classical logics, are presented.
Thirty-Seven undergraduate students (23 engineering students, 14 social and human science students) had their electroencephalogram (EEG) recorded during the performing of mental rotation and recognition of virtual tridimensional geometric patterns tasks. Their spatial cognition degree of development was assessed by a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from...
The problem of learning finite-state machines (FSM) is tackled by three Ant Colony Optimization (ACO) algorithms. The first two classical ACO algorithms are based on the classical ACO combinatorial problem reduction, where nodes of the ACO construction graph represent solution components, while full solutions are built by the ants in the process of foraging. The third recently introduced mutation-based...
Hydrothermal operational planning is categorized as an optimization problem that demands operational strategies of hydroelectric power plants in order to minimize the use of thermoelectric power plants, while maintaining the highest possible level of system's reservoirs during planning period. Moreover, the problem must meet a set of complex constraints. We showed in this paper that it is possible...
Power Distribution Network Reconfiguration demands the change of current state of the network in order to reach optimal operation according to some previouly defined figures of merit. This paper presents a new methodology based on Multi-Agent Systems for power distribution network reconfiguration aiming at minimizing power losses based on game theory. The principal characteristic of the game is the...
Recently, Bayesian networks became a popular technique to represent knowledge about uncertain domains and have been successfully used for applications in various areas. Even though there are several cases of success and Bayesian networks have been proved to be capable of representing uncertainty in many different domains, there are still two significant barriers to build large-scale Bayesian networks:...
Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi Algorithm, instead we present a computationally efficient algorithm that propagates the state probabilities through the image. This approach...
This work proposes a new methodology for the Group Recommendation problem. In this approach we choose the Most Representative User (MRU) as the group medoid in a user space projection, and then generate the recommendation list based on his preferences. We evaluate our proposal by using the well-known dataset Movie lens. We have taken two different measures so as to evaluate the group recommender strategies...
Real-time recognition of dynamic gestures is a problem for most of the applications nowadays. The prediction approach can be used as a solution for this. This approach uses an incomplete gesture input and it tries to predict which gesture the given input represents. This paper presents the application of the dynamic gesture feature extraction technique called Convexity Local Contour Sequence (CLCS)...
Artificial Neural Networks (ANN) have been widely used in time series forecasting problem. However, a more promising approach is the combination of ANN with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc, where these evolutionary algorithms have the objective of train and adjust all parameter of the ANN. In the evolutionary process is necessary define a fitness...
Artificial neural networks (ANN) have been paramount for modeling and forecasting time series phenomena. In this way it has been usual to suppose that each ANN model generates a white noise as prediction error. However, mostly because of disturbances not captured by each model, it is yet possible that such supposition is violated. On the other hand, to adopt a single ANN model may lead to statistical...
Traditionally, the countries classification is performed based on several features, that are related to economic and social factors. However, the classification process is costly due to the difficulty of obtaining those features and the need for intervention of human expertise. In this paper, we propose an intelligent agent that classifies countries based on financial indices. The artificial agent...
The Minimal Learning Machine (MLM) has been recently proposed as a novel supervised learning method for regression problems aiming at reconstructing the mapping between input and output distance matrices. Estimation of the response is then achieved from the geometrical configuration of the output points. Thanks to its comprehensive formulation, the MLM is inherently capable of dealing with nonlinear...
This paper reports modifications on a biologically inspired robotic architecture originally designed to work in single agent contexts. Several adaptations have been applied to the architecture, seeking as result a model-free artificial agent able to accomplish shared goals in a multiagent environment, from sensorial information translated into homeostatic variable values and a rule database that play...
This paper presents some strategies used for creating intelligent players of rock-paper-scissors using WiSARD weightless neural networks and results obtained therewith. These strategies included: (i) a new approach for encoding of the input data, (ii) three new training algorithms that allow the reclassification of the input patterns over time, (iii) a method for dealing with incomplete information...
It is surprising that last two decades many works in time series data mining and clustering were concerned with measures of similarity of time series but not with measures of association that can be used for measuring possible direct and inverse relationships between time series. Inverse relationships can exist between dynamics of prices and sell volumes, between growth patterns of competitive companies,...
This paper introduces a new approach to building sparse least square support vector machines (LSSVM) based on genetic algorithms (GAs) for classification tasks. LSSVM classifiers are an alternative to SVM ones due to the training process of LSSVM classifiers only requires to solve a linear equation system instead of a quadratic programming optimization problem. However, the lost of sparseness in the...
This paper examines a flexible flow shop problem that considers dynamic events, such as stochastic job arrivals, uncertain processing times, unexpected machine breakdowns and the possibility of processing flexibility. To achieve this goal, a new agent-based adaptive control system has been developed at the factory level, along with advanced decision-making strategies that provide responsive factories...
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