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Model aggregation is the process of constructing several base models that are then combined into a single model for prediction. Ensemble classification has been studied by many researchers and found [...]

Problem statement: In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence design problem is a multi-objective problem and it can be evaluated using four objective functions, namely, [H.sub.measure], similarity, continuity and hairpin. Approach: There are several ways to solve multi-objective problem, however, in order to evaluate the correctness of PSO algorithm in DNA sequence design, this problem is converted into single objective problem. Particle Swarm Optimization (PSO) is proposed to minimize the objective in the problem, subjected to two constraints: melting temperature and [GC.sub.content]. A model is developed to present the DNA sequence design based on PSO computation. Results: Based on experiments and researches done, 20 particles are used in the implementation of the optimization process, where the average values and the standard deviation for 100 runs are shown along with comparison to other existing methods. Conclusion: The results achieve verified that PSO can suitably solves the DNA sequence design problem using the proposed method and model, comparatively better than other approaches. Key words: Particle swarm optimization, DNA sequence design, optimization, user-defined weights, INTRODUCTION A nucleic acid is a macromolecule composed of chains of monomeric nucleotide. In biochemistry, these molecules carry genetic information or form structures within cells. The most common nucleic acids [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.10.020 Byline: Ayed Salman (a), Andries P. Engelbrecht (b), Mahamed G.H. Omran (c) Keywords: Evolutionary computations; Artificial intelligence; Differential evolution; Global optimization Abstract: Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a self-adaptive DE (SDE) algorithm which eliminates the need for manual tuning of control parameters is empirically analyzed. The performance of SDE is investigated and compared with other well-known approaches. The experiments conducted show that SDE generally outperform other DE algorithms in all the benchmark functions. Moreover, the performance of SDE using the ring neighborhood topology is investigated. Author Affiliation: (a) Computer Engineering Department, Kuwait University, Kuwait (b) Department of Computer Science, University of Pretoria, Pretoria, South Africa (c) Department of Computer Science, Gulf University for Science and Technology, Kuwait Article History: Received 9 October 2005; Accepted 11 October 2006

Abstract--An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method [...]

A manual approach to radiography process optimization is a time-consuming and labor-intensive process. Therefore, a virtual environment in which all of the processes of optimization for a desired radiography experiment or setup are conducted is highly desirable. Such an environment should be able to provide the capability to arrive at radiographic scanning parameters that are optimized to within preset criteria for design purposes. In this paper, a simplified approach toward achieving this is described, and calculated radiography results are benchmarked against experiments. A ray-tracing technique combined with the exponential law of attenuation was used to provide the primary function of such a virtual environment, which is the modeling of the radiography system. Radiography quality parameters such as contrast, penetration, unsharpness, and resolution were calculated using predefined definitions and fed directly into a particle swarm optimization routine that searched for the best radiography design parameters in an iterative feedback loop between the simulator and the optimizer modules. The aim of this paper is to show that a rather simple radiography simulation approach can already provide sufficient data for system design optimization purposes without the need to develop or utilize a comprehensive, competitive radiography simulator. The simplified approach provides a direct "uncomplicated" virtual environment for basic radiography training and basic experimental planning. [ABSTRACT FROM AUTHOR]