André Vargas Abs da Cruz

Quantum-Inspired Evolutionary Algorithms and Its Application to Numerical Optimization Problems

This work proposes a new kind of evolutionary algorithm inspired in the principles of quantum computing. This algorithm is an extension of a proposed model for combinatorial optimization problems which uses a binary representation for the chromosome. This extension uses probability distributions for each free variable of the problem, in order to simulate the superposition of solutions, which is intrinsic in the quantum computing methodology. A set of mathematical operations is used as implicit genetic operators over those probability distributions. The efficiency and the applicability of the algorithm are demonstrated through experimental results using the F6 function.

Cultural operators for a quantum-inspired evolutionary algorithm applied to numerical optimization problems

This work presents the application of cultural algorithms operators to a new quantum-inspired evolutionary algorithm with numerical representation. These operators (fission, fusion, generalization and specialization) are used in order to provide better control over the quantum-inspired evolutionary algorithm. We also show that the quantum-inspired evolutionary algorithm with numerical representation behaves in a very similar manner to a pure cultural algorithm and we propose further investigations concerning this aspect.

Quantum-Inspired Evolutionary Algorithms applied to numerical optimization problems

Since they were proposed as an optimization method, the evolutionary algorithms have been successfully used for solving complex problems in several areas such as, for example, the automatic design of electronic circuits and equipments, task planning and scheduling, software engineering and data mining, among many others. However, some problems are computationally intensive when it concerns the evaluation of solutions during the search process, making the optimization by evolutionary algorithms a slow process for situations where a quick response from the algorithm is desired (for instance, in online optimization problems). Several ways to overcome this problem, by speeding up convergence time, were proposed, including Cultural Algorithms and Coevolutionary Algorithms. However, these algorithms still have the need to evaluate many solutions on each step of the optimization process. In problems where this evaluation is computationally expensive, the optimization can take a prohibitive time to reach optimal solutions. This work presents an evolutionary algorithm for numerical optimization problems (Quantum-Inspired Evolutionary Algorithm for Problems based on Numerical Representation — QIEA-R), inspired in the concept of quantum superposition, which allows the optimization process to be carried on with a smaller number of evaluations. It extends previous works by presenting a broader range of tests and improvements on the algorithm. The results show the good performance of this algorithm in solving numerical problems.

A new model for credit approval problems: A quantum-inspired neuro-evolutionary algorithm with binary-real representation

This paper presents a new model for neuro-evolutionary systems. It is a new quantum-inspired evolutionary algorithm with binary-real representation (QIEA-BR) for evolution of a neural network. The proposed model is an extension of the QIEA-R developed for numerical optimization. The Quantum-Inspired Neuro-Evolutionary Computation model (QINEA-BR) is able to completely configure a feed-forward neural network in terms of selecting the relevant input variables, number of neurons in the hidden layer and all existent synaptic weights. QINEA-BR is evaluated in a benchmark problem of financial credit evaluation. The results obtained demonstrate the effectiveness of this new model in comparison with other machine learning and statistical models, providing good accuracy in separating good from bad customers.

Neural networks for inflow forecasting using precipitation information

This work presents forecast models for the natural inflow in the Basin of Iguacu River, incorporating rainfall information, based on artificial neural networks. Two types of rainfall data are available: measurements taken from stations distributed along the basin and ten-day rainfall forecasts using the ETA model developed by CPTEC (Brazilian Weather Forecating Center). The neural nework model also employs observed inflows measured by stations along the Iguacu River, as well as historical data of the natural inflows to be predicted. Initially, we applied preprocessing methods on the various series, filling missing data and correcting outliers. This was followed by methods for selecting the most relevant variables for the forecast model. The results obtained demonstrate the potential of using artificial neural networks in this problem, which is highly non-linear and very complex, providing forecasts with good accuracy that can be used in planning the hydroelectrical operation of the Basin.

Decision Support Methods

This section presents a summary of the main concepts on which evolutionary algorithms are based. First, the operating principle of Genetic Algorithms (GAs) is explained and their main parts and their evolution parameters described. Next, a description of Cultural Algorithms (CAs) is presented and its main components are pointed out.

NEVE++: A neuro-evolutionary unlimited ensemble for adaptive learning

In our previous works [1, 2], we proposed NEVE, a model that uses a weighted ensemble of neural network classifiers for adaptive learning, trained by means of a quantum-inspired evolutionary algorithm (QIEA). We showed that the neuro-evolutionary classifiers were able to learn the dataset and to quickly respond to any drifts on the underlying data. Now, we are particularly interested on analyzing the influence of an unlimited ensemble, instead of the limited ensemble from NEVE. For that, we modified NEVE to work with unlimited ensembles, and we call this new algorithm NEVE++. To verity how the unlimited ensemble influences the results, we used four different datasets with concept drift in order to compare the accuracy of NEVE and NEVE++, using two other existing algorithms as reference.

NEVE: A Neuro-Evolutionary Ensemble for Adaptive Learning

This work describes the use of a quantum-inspired evolutionary algorithm (QIEA-R) to construct a weighted ensemble of neural network classifiers for adaptive learning in concept drift problems. The proposed algorithm, named NEVE (meaning Neuro-EVolutionary Ensemble), uses the QIEA-R to train the neural networks and also to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running eight simulations using two different datasets and performing two different analysis of the results, we show that NEVE is able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results reached by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios.

Estimation of Distribution Algorithm Based on a Multivariate Extension of the Archimedean Copula

This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.

A2D2: A pre-event abrupt drift detection

Most drift detection mechanisms designed for classification problems works in a post-event manner: after receiving the data set completely (patterns and class labels of the train and test set), they apply a sequence of procedures to identify some change in the class-conditional distribution - a concept drift. However, detecting changes after its occurrence can be in some situations harmful for the process under supervision. This paper proposes a pre-event approach for abrupt drift detection, called by A2D2. Briefly, this method is composed of three steps: (i) label the patterns from the test set, using an unsupervised method; (ii) compute some statistics from the train and test set, conditioned on the given class labels; and (iii) compare the train and test statistics using a multivariate hypothesis test. Also, it has been proposed a procedure for creating datasets with abrupt drift. This procedure was used in the sensivity analysis of A2D2, in order to understand the influence degree of each parameter on its final performance.