THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Grey Wolf Optimizer

Data Mining Practical Machine Learning Tools and Techniques

The Whale Optimization Algorithm

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

Differential Evolution: A Survey of the State-of-the-Art

A Machine Learning-Based Approach for Economics-Tailored Applications: The Spanish Case Study

Editorial: Computación Evolutiva.

On the Effects of Structuring the Population

Software plays a central role in our life nowadays. We use it almost anywhere, at any time, and for everything: to browse the Internet, to check our emails, and even to access critical services such as health monitoring and banking. Hence, its reliability and general quality is critical. As software increases in complexity, developers spend more time fixing bugs or making code work rather than designing or writing new code. Thus, improving software understandability and maintainability would translate into an economic relief over the total cost of a project. Different cognitive complexity measures have been proposed to quantify the understandability of a piece of code and, therefore, its maintainability. However, the cognitive complexity metric provided by SonarSource and integrated in SonarCloud and SonarQube is quickly spreading in the software industry due to the popularity of these well-known static code tools for evaluating software quality. Despite SonarQube suggests to keep method’s cognitive complexity no greater than 15, reducing method’s complexity is challenging for a human programmer and there are no approaches to assist developers on this task. We model the cognitive complexity reduction of a method as an optimization problem where the search space contains all sequences of Extract Method refactoring opportunities. We then propose a novel approach that searches for feasible code extractions allowing developers to apply them, all in an automated way. This will allow software developers to make informed decisions while reducing the complexity of their code. We evaluated our approach over 10 open-source software projects and was able to fix 78% of the 1,050 existing cognitive complexity issues reported by SonarQube. We finally discuss the limitations of the proposed approach and provide interesting findings and guidelines for developers.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09686676.pdf

Automatizing Software Cognitive Complexity Reduction

High computational requirements of current problems have driven most researches towards efficient processing formulations which require the use of multiple processors interconnected, this is the foundation of the parallel processing mechanism. Among the metrics to measure the performance of parallel algorithms, the most important and used is the speedup, but in the scientific community does not exist a consent on its definition and use. The aim of this work is to study different alternatives evaluating parallel metaheuristics. This report presents the results of several experimental tests to show the use of the speedup evaluating the same parallel distributed Genetic Algorithm in different ways, to solve MAXSAT problem. Our experiments show that depending on how the algorithm speedup is evaluated, different results can be obtained. Taking into account the test results we can conclude that the best scenario for evaluating parallel algorithms is comparing algorithms with the same accuracy, defining the quality of the solutions as stop condition, because all executions reach the optimal value allowing fair comparisons.

Enrique Alba

Papers

A Machine Learning-Based Approach for Economics-Tailored Applications: The Spanish Case Study

Editorial: Computación Evolutiva.

On the Effects of Structuring the Population

Automatizing Software Cognitive Complexity Reduction

Influence of parallel metrics in the analysis of parallel metaheuristic algorithms