Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study

Application of an improved ant colony optimization algorithm of hybrid strategies using scheduling for patient management in hospitals

To balance the convergence speed and solution diversity and enhance optimization performance when addressing large-scale optimization problems, this research study presents an improved ant colony optimization (ICMPACO) technique. Its foundations include the co-evolution mechanism, the multi-population strategy, the pheromone diffusion mechanism, and the pheromone updating method. The suggested ICMPACO approach separates the ant population into elite and common categories and breaks the optimization problem into several sub-problems to boost the convergence rate and prevent slipping into the local optimum value. To increase optimization capacity, the pheromone update approach is applied. Ants emit pheromone at a certain spot, and that pheromone progressively spreads to a variety of nearby regions thanks to the pheromone diffusion process. Here, the real gate assignment issue and the travelling salesman problem (TSP) are chosen for the validation of the performance for the optimization of the ICMPACO algorithm. The experiment's findings demonstrate that the suggested ICMPACO method can successfully solve the gate assignment issue, find the optimal optimization value in resolving TSP, provide a better assignment outcome, and exhibit improved optimization ability and stability. The assigned efficiency is comparatively higher than earlier ones. With an assigned efficiency of 83.5 %, it can swiftly arrive at the ideal gate assignment outcome by assigning 132 patients to 20 gates of hospital testing rooms. To minimize the patient's overall hospital processing time, this algorithm was specifically employed with a better level of efficiency to create appropriate scheduling in the hospital.

A survey of evolutionary algorithms for supervised ensemble learning

This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

An improved software defect prediction model based on grey incidence analysis and Naive Bayes algorithm

This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction model. The grey incidence analysis is used to analyze the relation between software modules and ideal modules. Then, the grey correlation degree is embedded into the Naive Bayes classification model as a feature attribute. According to the comparison and analysis of NASA’s public dataset, the prediction model in this paper improves the prediction accuracy.

Estimation of COVID-19 epidemic curves using genetic programming algorithm

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R² scores of 0.999, while the models developed for estimation of recovered cases achieved the R² score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set.

Algorithm-supported, mass and sequence diversity-oriented random peptide library design

Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.

Empirical Study of Software Defect Prediction: A Systematic Mapping

Software defect prediction has been one of the key areas of exploration in the domain of software quality. In this paper, we perform a systematic mapping to analyze all the software defect prediction literature available from 1995 to 2018 using a multi-stage process. A total of 156 studies are selected in the first step, and the final mapping is conducted based on these studies. The ability of a model to learn from data that does not come from the same project or organization will help organizations that do not have sufficient training data or are going to start work on new projects. The findings of this research are useful not only to the software engineering domain, but also to the empirical studies, which mainly focus on symmetry as they provide steps-by-steps solutions for questions raised in the article.

Omni-Ensemble Learning (OEL): Utilizing Over-Bagging, Static and Dynamic Ensemble Selection Approaches for Software Defect Prediction

Machine learning methods in software engineering are becoming increasingly important as they can improve quality and testing efficiency by constructing models to predict defects in software modules. The existing datasets for software defect prediction suffer from an imbalance of class distribution which makes the learning problem in such a task harder. In this paper, we propose a novel approach by integrating Over-Bagging, static and dynamic ensemble selection strategies. The proposed method utilizes most of ensemble learning approaches called Omni-Ensemble Learning (OEL). This approach exploits a new Over-Bagging method for class imbalance learning in which the effect of three different methods of assigning weight to training samples is investigated. The proposed method first specifies the best classifiers along with their combiner for all test samples through Genetic Algorithm as the static ensemble selection approach. Then, a subset of the selected classifiers is chosen for each test sample as the dynamic ensemble selection. Our experiments confirm that the proposed OEL can provide better overall performance (in terms of G-mean, balance, and AUC measures) comparing with other six related works and six multiple classifier systems over seven NASA datasets. We generally recommend OEL to improve the performance of software defect prediction and the similar problem based on these experimental results.