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

This chapter contains sections titled: Introduction Multidimensional Knapsack Problem Hybrid Models Experimental Analysis Conclusions References

Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems

In this work we propose a new distributed evolutionary algorithm that uses a proactive strategy to adapt its migration policy and the mutation rate. The proactive decision is carried out locally in each subpopulation based on the entropy of that subpopulation. In that way, each subpopulation can change their own incoming flow of individuals by asking their neighbors for more frequent or less frequent migrations in order to maintain the genetic diversity at a desired level. Moreover, this proactive strategy is reinforced by adapting the mutation rate while the algorithm is searching for the problem solution. All these strategies avoid the subpopulations to get trapped into local minima. We conduct computational experiments on large instances of the NK landscape problem which have shown that our proactive approach outperforms traditional dEAs, particularly for not highly rugged landscapes, in which it does not only reaches the most accurate solutions, but it does the fastest.

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Enhancing distributed EAs by a proactive strategy

Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction

In our previous works, we empirically showed that a number of $$6_{\pm 2}$$6?2 informants may endow particle swarm optimization (PSO) with an optimized learning procedure in comparison with other combinations of informants. In this way, the new version PSO6, that evolves new particles from six informants (neighbors), performs more accurately than other existing versions of PSO and is able to generate good particles for a longer time. Despite this advantage, PSO6 may show certain attraction to local basins derived from its moderate performance on non-separable complex problems (typically observed in PSO versions). In this paper, we incorporate a local search procedure to the PSO6 with the aim of correcting this disadvantage. We compare the performance of our proposal (PSO6-Mtsls) on a set of 40 benchmark functions against that of other PSO versions, as well as against the best recent proposals in the current state of the art (with and without local search). The results support our conjecture that the (quasi)-optimally informed PSO, hybridized with local search mechanisms, reaches a high rate of success on a large number of complex (non-separable) continuous optimization functions.

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Hybrid PSO6 for hard continuous optimization

In this article we propose optimal and quasi optimal solutions to the problem of searching for the maximum lighting point inside a polygon P of n vertices. This problem is solved by using three different techniques: random search, simulated annealing and gradient. Our comparative study shows that simulated annealing is very competitive in this application. To accomplish the study, a new polygon generator has been implemented, which greatly helps in the general validation of our claims on the illumination problem as a new class of optimization task.

Enrique Alba

Papers

Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems

Enhancing distributed EAs by a proactive strategy

Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction

Hybrid PSO6 for hard continuous optimization

Solving the illumination problem with heuristics