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

Nowadays, it is common to find research problems (in system biology, mobile applications, etc) that change over time, requiring algorithms which dynamically adapt the search to the new conditions In most of them, the utilization of some information from the past allows to quickly adapt after a change This is the idea underlining the use of memory in this field, what involves key design issues concerning the memory content, the process of update, and the process of retrieval In this article, we focus on global memory schemes, which are the most intuitive and popular ones, and perform an integral analysis of current design variants based on a comprehensive set of benchmarks Results show the benefits and drawbacks of each strategy, as well as the effect of the algorithm and problem features in the memory performance

Global memory schemes for dynamic optimization

In this article we solve the radio network design problem (RND). This NP-hard combinatorial problem consist of determining a set of locations for placing radio antennae in a geographical area in order to offer high radio coverage using the smallest number of antennae. This problem is originally found in mobile telecommunications (such as mobile telephony), and is also relevant in the rising area of sensor networks. In this work we propose an evolutionary algorithm called CHC as the state of the art technique for solving RND problems and determine its expected performance for different instances of the RND problem.

/pdf/optimal-placement-of-antennae-using-metaheuristics-91foq8xufc.pdf

Optimal placement of antennae using metaheuristics

Measuring the performance is still an important unsolved issue in dynamic optimization. Although several measures have been proposed in the literature, the problem about which ones should be used to describe the behaviour of algorithms remains open. One of the aspects to be considered is whether fitness averages are able to summarize the overall performance of metaheuristics over dynamic problems. Another issue is how to compare algorithms and, more specifically, how to quantify the numerical difference in performance between them. The main goal in this article is to propose a new way of measuring the behaviour of algorithms and also to provide a method to quantify the distance between them. We introduce thus two measures: one based on the area below the curve defined by some population property at each generation (e.g., the best-of-generation fitness), and a second one based on the area between the curves of two different algorithms.

ABC, a new performance tool for algorithms solving dynamic optimization problems

Research on multiobjective optimization is very active currently because most of the real-world engineering optimization problems are multiobjective in nature. Multiobjective optimization does not restrict to find a unique single solution, but a set of solutions collectively known as the Pareto front. Evolutionary algorithms (EAs) are especially well-suited for solving such kind of problems because they are able to find multiple trade-off solutions in a single run. However, these algorithms may be computationally expensive because (1) real-world problem optimization typically involves tasks demanding high computational resources and (2) they are aimed at finding the whole front of optimal solutions instead of searching for a single optimum. Parallelizing EAs arises as a possible way of facing this drawback. The first goal of this chapter is to provide the reader with a wide overview of the literature on parallel EAs for multiobjective optimization. Later, we include an experimental study where we develop and analyze pPAES, a parallel EA for multiobjective optimization based on the Pareto Archived Evolution Strategy (PAES). The obtained results show that pPAES is a promising option for solving multiobjective optimization problems.

Parallel Evolutionary Multiobjective Optimization

In this work we address the parallelization of the kind of Evolutionary Algorithms (EAs) known as Estimation of Distribution Algorithms (EDAs). After an initial discussion on the types of potentially parallel schemes for EDAs, we proceed to design a distributed island version (dEDA), aimed at improving the numerical efficiency of the sequential algorithm in terms of the number of evaluations. After evaluating such a dEDA on several well-known discrete and continuous test problems, we conclude that our model clearly outperforms existing centralized approaches from a numerical point of view, as well as speeding up the search considerably, thanks to its suitability for physical parallelism.

Enrique Alba

Papers

Global memory schemes for dynamic optimization

Optimal placement of antennae using metaheuristics

ABC, a new performance tool for algorithms solving dynamic optimization problems

Parallel Evolutionary Multiobjective Optimization

A Parallel Island Model for Estimation of Distribution Algorithms