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.

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

Many specific algorithms and metaheuristics have been proposed for solving the DNA fragment assembly problem, but new algorithms with more capacity for solving this problem are necessary. The fragment assembly problem consists in building the DNA sequence from several hundreds (or even, thousands) of fragments obtained by biologists in the laboratory. This is an important task in any genome project since the rest of the phases depend on the accuracy of the results of this stage. In order to achieve this objective we propose a hybrid algorithm that achieves very accurate results in comparison with other metaheuristics.

Variable Neighborhood Search as Genetic Algorithm Operator for DNA Fragment Assembling Problem

In 2019, around 57% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a half hours online every day. But in spite of being a great amount of time, those resources are idle most of the day. Therefore, taking advantage of them presents an interesting opportunity. In this study, we introduce JSDoop, a prototype implementation to profit from this opportunity. In particular, we propose a volunteer web browser-based high-performance computing library. JSdoop divides a problem into tasks and uses different queues to distribute the computation. Then, volunteers access the web page of the problem and start processing the tasks in their web browsers. We conducted a proof-of-concept using our proposal and TensorFlow.js to train a recurrent neural network that predicts text. We tested it in a computer cluster and with up to 32 volunteers. The experimental results show that training a neural network in distributed web browsers is feasible and accurate, has a high scalability, and it is an interesting area for research.

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Vehicular ad hoc networks (VANETs) are self-organizing communication networks, which principally consist of vehicles that broadcast beacons with relevant real time traffic information. These continuous exchanges of information allow the development of applications that drastically improve the road traffic safety and efficiency. Such services suffer from network congestion problems when the road traffic density increases. This may cause VANET malfunction, and thus, the increase of hazardous road situations. In this study, we present a family of fully distributed intelligent light-weight congestion control algorithms (executed by each node), i.e., the Distributed Intelligent Fair Rate Adaptation (DIFRA) family. These methods accurately estimate the channel load in a distributed manner and dynamically adapt the beacon rate of each node. Experimental analyses show the effectiveness of DIFRA methods in increasing the amount of data exchanged between the vehicles and the balance in the channel usage, while avoiding network congestion.

Distributed Fair Rate Congestion Control for Vehicular Networks

In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all products, e.g., all pairs of features (pairwise coverage). In addition, it is desirable to first test products composed by a set of priority features. This problem is known as the Prioritized Pairwise Test Data Generation Problem. In this work we propose two hybrid algorithms using Integer Programming (IP) to generate a prioritized test suite. The first one is based on an integer linear formulation and the second one is based on a integer quadratic (nonlinear) formulation. We compare these techniques with two state-of-the-art algorithms, the Parallel Prioritized Genetic Solver (PPGS) and a greedy algorithm called prioritized-ICPL. Our study reveals that our hybrid nonlinear approach is clearly the best in both, solution quality and computation time. Moreover, the nonlinear variant (the fastest one) is 27 and 42 times faster than PPGS in the two groups of instances analyzed in this work.

Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines

We present in this work a wide spectrum of results on analyzing the behavior of parallel heuristics (both pure and hybrid) for solving optimization problems. We focus on several evolutionary algorithms as well as on simulated annealing. Our goal is to offer a first study on the possible changes in the search mechanics that the algorithms suffer when shifting from a LAN network to a WAN environment. We will address six optimization tasks of considerable complexity. The results show that, despite their expected slower execution time, the WAN versions of our algorithms consistently solve the problems. We report also some interesting results in which WAN algorithms outperform LAN ones. Those results are further extended to analyze the behavior of the heuristics in WAN with a larger number of processors and different connectivities.

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Enrique Alba

Papers

Variable Neighborhood Search as Genetic Algorithm Operator for DNA Fragment Assembling Problem

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Distributed Fair Rate Congestion Control for Vehicular Networks

Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines

Parallel LAN/WAN heuristics for optimization