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

Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GAs) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GAs (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming of sequential GAs. In the case of applying PGAs, we can expect better performance, the reason being the exchange of knowledge during the parallel search process. The resulting distributed search is different compared to what sequential panmictic GAs do, then deserving additional studies. This article presents a performance study of three different PGAs. Moreover, we investigate the effect of synchronizing communications over modern shared-memory multiprocessors. We consider the master-slave model along with synchronous and asynchronous distributed GAs (dGAs), presenting their different designs and expected similarities when running in a number of cores ranging from one to 32 cores. The master-slave model showed a competitive numerical effort versus the other dGAs and demonstrated to be able to scale-up well over multiprocessors. We describe how the speed-up and parallel performance of the dGAs is changing as the number of cores enlarges. Results of the island model show that synchronous and asynchronous dGAs have different numerical performances on a multiprocessor, the asynchronous algorithm having a faster execution, thus more attractive for time demanding applications. Our results and statistical analyses help in developing a novel body of knowledge on PGAs running in shared memory multiprocessors (versus overwhelming literature oriented to distributed memory clusters), something useful for researchers, beginners, and final users of these techniques.

Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors

The Test Suite Minimization problem in regression testing is a software engineering problem which consists in selecting a set of test cases from a large test suite that satisfies a given condition, like maximizing the coverage and/or minimizing the oracle cost. In this work we use an approach based on SAT solvers to find optimal solutions for the Test Suite Minimization Problem. The approach comprises two translations: from the original problem instance into Pseudo-Boolean constraints and then to a propositional Boolean formula. In order to solve a problem, we first translate it into a SAT instance. Then the SAT instance is solved using a state-of-the-art SAT solver. Our main contributions are: we create an encoding for single and multi-objective formulations of the Test Suite Minimization Problem as Pseudo-Boolean constraints and we compute optimal solutions for well-known and highly-used instances of this problem for future reference.

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On the application of SAT solvers to the test suite minimization problem

Ant Colony Optimisation (ACO) algorithms are intrinsically distributed algorithms where independent agents are in charge of building solutions collaboratively. Stigmergy or indirect communication is the way in which each agent learns from the experience of the whole colony. In this sense, explicit communication models of ACO can be defined directly giving birth to parallel algorithms of high numerical and real time efficiency. We do so in this work and apply the resulting algorithms to the Minimum Tardy Task Problem (MTTP), a scheduling problem that has been faced with other metaheuristics in the past. The aim of this paper is to report experimental results on the behaviour of two types of parallel ACO algorithms on large instances of the mentioned problem with the goal of improving existing solutions significantly.

Two models of parallel ACO algorithms for the minimum tardy task problem

Measuring the Performance of Parallel Metaheuristics

In recent years, graphics processing units (GPUs) have emerged as a powerful architecture for solving a broad spectrum of applications in very short periods of time. However, most existing GPU optimization approaches do not exploit the full power available in a CPU–GPU platform. They have a tendency to leave one of them partially unused (usually the CPU) and fail to establish an accurate exchange of information that could help solve the target problem efficiently. Thus, better performance is expected from devising a hybrid CPU–GPU parallel algorithm that combines the highly parallel stream processing power of GPUs with the higher power of multi-core architectures. We have developed a hybrid methodology to efficiently solve optimization problems. We use a hybrid CPU–GPU architecture, to benefit from running it, in parallel, on both the CPU and the GPU. Our experiments over a heterogeneous set of combinatorial optimization problems with increasing dimensionality show a time gain of up to $$365\times $$
 in our proposal, while demonstrating high numerical accuracy. This work is intended to open up a new line of research that matches both architectures with new algorithms and cooperation techniques.

Enrique Alba

Papers

Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors

On the application of SAT solvers to the test suite minimization problem

Two models of parallel ACO algorithms for the minimum tardy task problem

Measuring the Performance of Parallel Metaheuristics

Solving optimization problems using a hybrid systolic search on GPU plus CPU