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

Ant Colony Optimization (ACO) has been successfully applied to those combinatorial optimization problems which can be translated into a graph exploration. Artificial ants build solutions step by step adding solution components that are represented by graph nodes. The existing ACO algorithms are suitable when the graph is not very large (thousands of nodes) but is not useful when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it. In this paper we study a new ACO model that overcomes the difficulties found when working with a huge construction graph. In addition to the description of the model, we analyze in the experimental section one technique used for dealing with this huge graph exploration. The results of the analysis can help to understand the meaning of the new parameters introduced and to decide which parameterization is more suitable for a given problem. For the experiments we use one real problem with capital importance in Software Engineering: refutation of safety properties in concurrent systems. This way, we foster an innovative research line related to the application of ACO to formal methods in Software Engineering.

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ACOhg: dealing with huge graphs

Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into a professional tool for CIT.

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Evolutionary algorithm for prioritized pairwise test data generation

The availability of low cost powerful parallel graphic cards has estimulated a trend to implement diverse algorithms on Graphic Processing Units (GPUs). In this paper we describe the design of a parallel Cellular Genetic Algorithm (cGA) on a GPU and then evaluate its performance. Beyond the existing works on masterslave for fitness evaluation, we here implement a cGA exploiting data and instructions parallelism at the population level. Using the CUDA language on a GTX-285 GPU hardware, we show how a cGA can profit from it to create an algorithm of improved physical efficiency and numerical efficacy with respect to a CPU implementation. Our approach stores individuals and their fitness values in the globalmemory of the GPU. Both, fitness evaluation and genetic operators are implemented entirely on GPU (i.e. no CPU is used). The presented approach allows us benefit from the numerical advantages of cGAs and the efficiency of a low-cost but powerful platform.

Cellular Genetic Algorithm on Graphic Processing Units

The Frequency Assignment Problem (FAP) is one of the key issues in the design of GSM networks (Global System for Mobile communications), and will remain important in the foreseeable future. There are many versions of FAP, most of them benchmarking-like problems. We use a formulation of FAP, developed in published work, that focuses on aspects which are relevant for real-world GSM networks. In this paper, we have designed, adapted, and evaluated several types of metaheuristic for different time ranges. After a detailed statistical study, results indicate that these metaheuristics are very appropriate for this FAP. New interference results have been obtained, that significantly improve those published in previous research.

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Metaheuristics for solving a real-world frequency assignment problem in GSM networks

A genetic programming approach to fuzzy logic controller (FLC) design is presented in this paper. We propose an encoding that represents fuzzy rule-bases as type-constrained syntactic trees implemented as variable-length strings. This encoding is applied to the cart centering problem and compared with other approaches: an intuitive FLC done by hand, an FLC obtained by a traditional genetic algorithm operating on fixed length strings and the analytical optimal solution (bang-bang rule). The results of the GP model are better (158 time steps) than the intuitive solution (249 time steps) and comparable with the GA solution (149 time steps). The analytical optimal solution docks the cart in 129 time steps.

Enrique Alba

Papers

ACOhg: dealing with huge graphs

Evolutionary algorithm for prioritized pairwise test data generation

Cellular Genetic Algorithm on Graphic Processing Units

Metaheuristics for solving a real-world frequency assignment problem in GSM networks

Type-constrained genetic programming for rule-base definition in fuzzy logic controllers