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Abstract: Estimation of distribution algorithms (EDAs) identify linkages among genes and build models which decompose a given problem. EDAs have been successfully applied to many real-world problems; however, whether their models indicate the optimal way to decompose the given problem is rarely studied. This paper proposes using the number of function evaluations () as the performance measure of EDA models. As a result, the optimal model can be defined as the one that consumes the fewest on average for EDAs to solve a specific problem. Based on this concept, correct building blocks (BBs) can be defined as groups of genes that construct the optimal model. Similarly, linkages within a BB are defined as the correct linkages of which the specific problem consists. The capabilities of four commonly used linkage-learning metrics, nonlinearity, entropy, simultaneity and differential mutual complement, are investigated based on the above definitions. For certain partially separable problems, none of the above metrics yields difference that is statistically significant between linear and nonlinear gene pairs. Although an optimal threshold still exists to separate linear and nonlinear gene pairs, most existing EDA designs today have not yet characterize such threshold. Based on the idea of estimation, this paper also proposes a metric enhancer, named eNFE, to enhance existing linkage-learning techniques. Empirical results show that eNFE improves BB identification by eliminating spurious linkages which occur often in most existing EDAs. [Copyright &y& Elsevier]

Abstract: It is well known that data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. A proper instantiation of a data structure can greatly impact the performance and the efficiency of the software system. In this paper we are focusing on the problem of customizing data structures instances during the execution of software systems using a supervised learning approach. In order to customize a data structure instance according to the software system’s current execution context, a neural network will be used. We experimentally evaluated our technique on a case study, emphasizing the advantages of the proposed approach. The obtained experimental results highlight the potential of our proposal in using a supervised learning based approach for dynamically configuring data structures instances. [Copyright &y& Elsevier]

Highlights: [•] This paper presents a new multiobjective swarm hybrid learning algorithm, MPSON. [•] We also propose MGAN to comparison with MPSON based on performance measures. [•] The aim of our algorithms is to obtain simple and accurate RBF networks simultaneously. [•] We validate the algorithms on datasets with different features, samples and classes. [•] Results show MPSON is the best algorithm with the accuracy-complexity balance for RBF network design. [Copyright &y& Elsevier]

Abstract: Classification is one of the key issues in the fields of decision sciences and knowledge discovery. In this paper, we present a new classification method based on gravitational potential energy between particles. The basic principle of gravitation based classification (GBC) algorithm is to find the equilibrium condition of the classifier, which is modeled as a classifier line between two groups of fixed particles. In the proposed approach, the input data is a collection of masses, which interact with each other based on Newton’s universal law of gravitation and the laws of motion. We present a convex formulation for this problem that always converges to a global optimum solution. The proposed method has been compared with some well-known classification approaches, and the results confirm the high performance of the proposed method. [Copyright &y& Elsevier]

Abstract: The machine learning community has traditionally used correct classification rates or accuracy (C) values to measure classifier performance and has generally avoided presenting classification levels of each class in the results, especially for problems with more than two classes. C values alone are insufficient because they cannot capture the myriad of contributing factors that differentiate the performance of two different classifiers. Receiver Operating Characteristic (ROC) analysis is an alternative to solve these difficulties, but it can only be used for two-class problems. For this reason, this paper proposes a new approach for analysing classifiers based on two measures: C and sensitivity (S) (i.e., the minimum of accuracies obtained for each class). These measures are optimised through a two-stage evolutionary process. It was conducted by applying two sequential fitness functions in the evolutionary process, including entropy (E) for the first stage and a new fitness function, area (A), for the second stage. By using these fitness functions, the C level was optimised in the first stage, and the S value of the classifier was generally improved without significantly reducing C in the second stage. This two-stage approach improved S values in the generalisation set (whereas an evolutionary algorithm (EA) based only on the S measure obtains worse S levels) and obtained both high C values and good classification levels for each class. The methodology was applied to solve 16 benchmark classification problems and two complex real-world problems in analytical chemistry and predictive microbiology. It obtained promising results when compared to other competitive multi-class classification algorithms and a multi-objective alternative based on E and S. [Copyright &y& Elsevier]

Abstract: This paper presents a new feature discovery approach called FEADIS that strengthens learning algorithms with discovered features. The discovered features are formed by various mathematical functions including ceil, mod, sin, and similar. These features are constructed in an iterative manner to improve gradually its learning performance. We demonstrate FEADIS capabilities by testing different types of datasets including periodical datasets. From the results, we conclude that FEADIS increases the performance of learning algorithms in a wide range of datasets for nominal or numeric target feature. Furthermore, most of the well known classifiers without FEADIS strengthening have severe difficulty in handling datasets that have periodical functional relations between input features and target feature – a difficulty circumvented by their potential use of FEADIS. [Copyright &y& Elsevier]