Beatrice Lazzerini

A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems

In this paper, we propose the use of a multiobjective evolutionary approach to generate a set of linguistic fuzzy-rule-based systems with different tradeoffs between accuracy and interpretability in regression problems. Accuracy and interpretability are measured in terms of approximation error and rule base (RB) complexity, respectively. The proposed approach is based on concurrently learning RBs and parameters of the membership functions of the associated linguistic labels. To manage the size of the search space, we have integrated the linguistic two-tuple representation model, which allows the symbolic translation of a label by only considering one parameter, with an efficient modification of the well known (2 + 2) Pareto archived evolution strategy (PAES). We tested our approach on nine real world datasets of different sizes and with different numbers of variables. Besides the (2 + 2)PAES, we have also used the well known nondominated sorting genetic algorithm (NSGA-II) and an accuracy-driven single-objective evolutionary algorithm (EA). We employed these optimization techniques both to concurrently learn rules and parameters and to learn only rules. We compared the different approaches by applying a nonparametric statistical test for pairwise comparisons, thus taking into consideration three representative points from the obtained Pareto fronts in the case of the multiobjective EAs. Finally, a data complexity measure, which is typically used in pattern recognition to evaluate the data density in terms of average number of patterns per variable, has been introduced to characterize regression problems. Results confirm the effectiveness of our approach, particularly for (possibly high dimensional) datasets with high values of the complexity metric.

A genetic algorithm for generating optimal assembly plans

Abstract In this paper, we propose a genetic algorithm that generates and assesses assembly plans. An appropriately modified version of the well-known partially matched crossover, and purposely defined mutation operators allow the algorithm to produce near-optimal assembly plans starting from a randomly initialised population of (possibly non-feasible) assembly sequences. The quality of a feasible assembly sequence is evaluated based on the following three optimisation criteria: (i) minimising the orientation changes of the product; (ii) minimising the gripper replacements; and (iii) grouping technologically similar assembly operations. Two examples that endorse the soundness of our approach are also included.

A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems

In the last years, the numerous successful applications of fuzzy rule-based systems (FRBSs) to several different domains have produced a considerable interest in methods to generate FRBSs from data. Most of the methods proposed in the literature, however, focus on performance maximization and omit to consider FRBS comprehensibility. Only recently, the problem of finding the right trade-off between performance and comprehensibility, in spite of the original nature of fuzzy logic, has arisen a growing interest in methods which take both the aspects into account. In this paper, we propose a Pareto-based multi-objective evolutionary approach to generate a set of Mamdani fuzzy systems from numerical data. We adopt a variant of the well-known (2+2) Pareto Archived Evolutionary Strategy ((2+2)PAES), which adopts the one-point crossover and two appropriately defined mutation operators. (2+2)PAES determines an approximation of the optimal Pareto front by concurrently minimizing the root mean squared error and the complexity. Complexity is measured as sum of the conditions which compose the antecedents of the rules included in the FRBS. Thus, low values of complexity correspond to Mamdani fuzzy systems characterized by a low number of rules and a low number of input variables really used in each rule. This ensures a high comprehensibility of the systems. We tested our version of (2+2)PAES on three well-known regression benchmarks, namely the Box and Jenkins Gas Furnace, the Mackey-Glass chaotic time series and Lorenz attractor time series datasets. To show the good characteristics of our approach, we compare the Pareto fronts produced by the (2+2)PAES with the ones obtained by applying a heuristic approach based on SVD-QR decomposition and four different multi-objective evolutionary algorithms.

Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.

Generation of Optimized Assembly Sequences Using Genetic Algorithms

Abstract This paper describes a method based on genetic algorithms for the generation and the evaluation of assembly sequences. Genetic algorithms are here used to drastically reduce the high computational time, usually necessary to evaluate the best assembly sequences, owing to ‘combinatorial explosion’ phenomena. The generation of optimized sequences is performed using an appropriate fitness function which takes into account simultaneously the geometrical constraints, the minimization of gripper changes and object orientations, and the possibility of grouping similar assembly operations (screwing, pressing, etc.). The paper also presents the chromosome structure used in the system, the genetic operators and, finally, a meaningful example of application.

Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets

We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms of sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents of the classifier rules). Then, we use the ROC convex hull method to select the potentially optimal classifiers in the projection of the Pareto front approximation onto the ROC plane. Our method was tested on 13 highly imbalanced datasets and compared with 2 two-objective evolutionary approaches and one heuristic approach to FRBC generation, and with three well-known classifiers. We show by the Wilcoxon signed-rank test that our three-objective optimization approach outperforms all the other techniques, except for one classifier, in terms of the area under the ROC convex hull, an accuracy measure used to globally compare different classification approaches. Further, all the FRBCs in the ROC convex hull are characterized by a low value of complexity. Finally, we discuss how, the misclassification costs and the class distributions are fixed, we can select the most suitable classifier for the specific application. We show that the FRBC selected from the convex hull produced by our three-objective optimization approach achieves the lowest classification cost among the techniques used as comparison in two specific medical applications.

Real-Time Detection of Traffic From Twitter Stream Analysis

Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.

Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

In this paper we propose a multi-objective evolutionary algorithm to generate Mamdani fuzzy rule-based systems with different good trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we introduce the concepts of virtual and concrete rule bases: the former is defined on linguistic variables, all partitioned with a fixed maximum number of fuzzy sets, while the latter takes into account, for each variable, a number of fuzzy sets as determined by the specific partition granularity of that variable. We exploit a chromosome composed of two parts, which codify the variables partition granularities, and the virtual rule base, respectively. Genetic operators manage virtual rule bases, whereas fitness evaluation relies on an appropriate mapping strategy between virtual and concrete rule bases. The algorithm has been tested on two real-world regression problems showing very promising results.

An electronic nose for odour annoyance assessment

Although in most cases annoying atmospheric emissions do not menace public health, they are less and less tolerated because of the effects on quality of life. Several approaches have been proposed to face this problem but none of them offers a completely satisfying solution. The development of electronic noses, which promise to mimic human sense of smell by means of a sensor array and a pattern recognition model, offers new interesting perspectives. In this paper, an electronic nose based on conducting polymer sensors and a fuzzy logic-based pattern recognition system is tested with waste water samples, obtaining 87% recognition rate on the test set. Current limits of this new technology are discussed and a strategy for their overcoming is proposed.

24-hour-ahead forecasting of energy production in solar PV systems

This paper presents a flexible approach to forecasting of energy production in solar photovoltaic (PV) installations, using time series analysis and neural networks. Our goal is to develop a one day-ahead forecasting model based on an artificial neural network with tapped delay lines. Despite some methods already exist for energy forecasting problems, the main novelty of our approach is the proposal of a tool for the technician of a PV installation to correctly configure the forecasting model according to the particular installation characteristics. The correct configuration takes into account the number of hidden neurons, the number of delay elements, and the training window width, i.e., the appropriate number of days, before the predicted day, employed for the training. The irradiation along with the sampling hour are used as input variables to predict the daily accumulated energy with a percentage error less than 5%.