Adriano Soares Koshiyama

GPFIS-CONTROL: A GENETIC FUZZY SYSTEM FOR CONTROL TASKS

Abstract This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFIS-Control). It is based on Multi-Gene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFIS-Control are considered: the Cart-Centering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFIS-Control in relation to other GFCs found in the literature.

GPF-CLASS: A Genetic Fuzzy model for classification

This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn “if-then” fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.

Evolutionary features and parameter optimization of spiking neural networks for unsupervised learning

This paper introduces two new hybrid models for clustering problems in which the input features and parameters of a spiking neural network (SNN) are optimized using evolutionary algorithms. We used two novel evolutionary approaches, the quantum-inspired evolutionary algorithm (QIEA) and the optimization by genetic programming (OGP) methods, to develop the quantum binary-real evolving SNN (QbrSNN) and the SNN optimized by genetic programming (SNN-OGP) neuro-evolutionary models, respectively. The proposed models are applied to 8 benchmark datasets, and a significantly higher clustering accuracy compared to a standard SNN without feature and parameter optimization is achieved with fewer iterations. When comparing QbrSNN and SNN-OGP, the former performed slightly better but at the expense of increased computational effort.

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.

Combining Forecasts: A Genetic Programming Approach

Combining forecasts is a common practice in time series analysis. This technique involves weighing each estimate of different models in order to minimize the error between the resulting output and the target. This work presents a novel methodology, aiming to combine forecasts using genetic programming, a metaheuristic that searches for a nonlinear combination and selection of forecasters simultaneously. To present the method, the authors made three different tests comparing with the linear forecasting combination, evaluating both in terms of RMSE and MAPE. The statistical analysis shows that the genetic programming combination outperforms the linear combination in two of the three tests evaluated.

Automatic Synthesis of Fuzzy Inference Systems for Classification

This work introduces AutoFIS-Class, a methodology for automatic synthesis of Fuzzy Inference Systems for classification problems. It is a data-driven approach, which can be described in five steps: (i) mapping of each pattern to a membership degree to fuzzy sets; (ii) generation of a set of fuzzy rule premises, inspired on a search tree, and application of quality criteria to reduce the exponential growth; (iii) association of a given premise to a suitable consequent term; (iv) aggregation of fuzzy rules to a same class and (v) decision on which consequent class is most compatible with a given pattern. The performance of AutoFIS-Class has been compared to those of other four rule-based systems for 21 datasets. Results show that AutoFIS-Class is competitive with respect to those systems, most of them evolutionary ones.

A Novel Genetic Fuzzy System for Regression Problems

Solving a regression problem is equivalent to finding a model that relates the behavior of an output or response variable to a given set of input or explanatory variables. An example of such a problem would be that of a company that wishes to evaluate how the demand for its product varies in accordance to its and other competitors’ prices. Another example could be the assessment of an increase in electricity consumption due to weather changes. In such problems, it is important to obtain not only accurate predictions but also interpretable models that can tell which features, and their relationship, are the most relevant. In order to meet both requirements—linguistic interpretability and reasonable accuracy—this work presents a novel Genetic Fuzzy System (GFS), called Genetic Programming Fuzzy Inference System for Regression problems (GPFIS-Regress). This GFS makes use of Multi-Gene Genetic Programming to build the premises of fuzzy rules, including in it t-norms, negation and linguistic hedge operators. In a subsequent stage, GPFIS-Regress defines a consequent term that is more compatible with a given premise and makes use of aggregation operators to weigh fuzzy rules in accordance with their influence on the problem. The system has been evaluated on a set of benchmarks and has also been compared to other GFSs, showing competitive results in terms of accuracy and interpretability issues.

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.

GPFIS-Control: A fuzzy Genetic model for Control tasks

This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFIS-Control). It is based on Multi-Gene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFIS-Control are considered: the Cart-Centering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFIS-Control in relation to other GFCs found in the literature.