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

Dynamic optimization problems (DOPs) have proven to be a realistic model of dynamic environments where the fitness function, problem parameters, and/or problem constraints are subject to changes. Evolutionary algorithms (EAs) are getting pride of place in solving DOPs due to their ability to match with Nature evolution processes. Several approaches have been presented over the years to enhance the performance of EAs to locate the moving optima in the landscape and avoid premature convergence. We address in this chapter a new bi-population EA augmented by a memory of past solutions and validate it with the dynamic knapsack problem (DKP). We suggest, through the use of two populations, to conduct the search to different directions in the problem space: the first population takes in charge exploring while the second population is responsible for exploiting. Once an environment change is detected, knowledge acquired from the old environment is stored in order to recall it whenever the same state reappears. We illustrate our study by presenting several experiments and compare our results to those of standard algorithms.

BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems

Landscape theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of a special kind of landscapes called elementary landscapes. The decomposition of the objective function of a problem into its elementary components can be exploited to compute summary statistics. We present closed-form expressions for the fitness-distance correlation (FDC) based on the elementary landscape decomposition of the problems defined over binary strings in which the objective function has one global optimum. We present some theoretical results that raise some doubts on using FDC as a measure of problem difficulty.

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Exact computation of the fitness-distance correlation for pseudoboolean functions with one global optimum

Road transportation is becoming a major concern in modern cities. The growth of the number of vehicles is provoking an important increment of pollution and greenhouse gas emissions generated by road traffic. In this paper, we present CTPATH, an innovative smart mobility software system that offers efficient paths to drivers in terms of travel time and greenhouse gas emissions. In order to obtain accurate results, CTPATH computes these paths taking into account the layout and habits in the city and real-time road traffic data. It offers customized paths to drivers including personal profiles in a distributed and intelligent way so as to consider the whole city situation.

CTPATH: A Real World System to Enable Green Transportation by Optimizing Environmentaly Friendly Routing Paths

A cellular genetic algorithm (cGA) is a powerful metaheuristic that has been successfully used since its creation to solve optimization problems. Over the past few years, interest in hybrid metaheuristics has also grown considerably. Research into cross fertilization between algorithms has provided extremely efficient search techniques in the past. In this paper we present a new way of hybridizing a metaheuristic through active components of other metaheuristics. We also introduce a novel methodology for identifying what an active component is. The active components detected are later inserted in a host metaheuristic so as to enhance its performance with regards to efficiency and accuracy (computational symbiosis). In the approach presented here we enhance a cGA, the host metaheuristic, with identified active components of other metaheuristics. After using this computational symbiosis, we analyze the performance of the new resulting algorithms by evaluating them on a set of different well-known discrete problems. The results obtained are objectively satisfactory in efficacy and efficiency.

Active components of metaheuristics in cellular genetic algorithms

This chapter presents an in-depth study of a novel parallel optimization algorithm specially designed to run on Graphic Processing Units (GPUs). The underlying operation relates to systolic computing and is inspired by the systolic contraction of the heart that makes possible blood circulation. The algorithm, called Systolic Genetic Search (SGS), is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs to achieve high time performance. SGS has shown not only to numerically outperform a random search and two genetic algorithms for solving the Knapsack Problem over a set of increasingly sized instances, but also its parallel implementation can obtain a runtime reduction that, depending on the GPU technology used, can reach more than 100 times. A study of the performance of the parallel implementation of SGS on four different GPUs has been conducted to show the impact of the Nvidia’s GPU compute capabilities on the runtimes of the algorithm.

Enrique Alba

Papers

BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems

Exact computation of the fitness-distance correlation for pseudoboolean functions with one global optimum

CTPATH: A Real World System to Enable Green Transportation by Optimizing Environmentaly Friendly Routing Paths

Active components of metaheuristics in cellular genetic algorithms

New Ideas in Parallel Metaheuristics on GPU: Systolic Genetic Search