Device robust-design is inherently a multiple-objective optimization problem. Using design of experiments (DoE) combined with response surface methodology (RSM) can satisfy the great incentive to reduce the number of TCAD simulations that need to be performed. However, the errors of RSM models may large enough to diminish the validity of the results for some nonlinear problems. To find the feas...

In this paper, we present a new mutation operator, Hybrid Mutation (HPRM), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The Hybrid Mutation operator constructs an offspring from a pair of parents by hybridizing two mutation operators, PSM and RSM. The efficiency of the HPRM is compared as against some existing mutation operators; namely,...

R surface methodology (RSM) is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is popular in practice, its current appl...

Biofiltration is emerging as a promising cost effective technique for the Volatile Organic Compounds (VOCs) removal from industrial waste gases. In the present investigation a comparative modeling study has been carried out using Radial Basis Function Neural Network (RBFN) and Response Surface Methodology (RSM) to predict and optimize the performance of a biofilter system treating toluene (a mo...

Response Surface Methodology (RSM) is a popular class of methods used to build empirical models through statistical means. Rarely in the literature is the RSM applied to model characteristics of Evolutionary Algorithm (EA) based techniques used to solve dynamic optimization problems. This paper presents RSM as a modelling tool of some characteristics of a memory and EA-based approach referred t...

The response surface methodology (RSM), which typically uses quadratic polynomials, is predominantly used for metamodeling in crashworthiness optimization because of the high computational cost of vehicle crash simulations. Research shows, however, that RSM may not be suitable for modeling highly nonlinear responses that can often be found in impact related problems, especially when using limit...

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology (RSM). The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951–1975, during which the so-called classical RSM was developed. This includes a review of basic experi...

Xanthomonas oryzae pv. oryzae, the causal agent of bacterial leaf blight in rice, contains a regulator that is encoded in the genome, designated OryR, which belongs to the N-acyl homoserine lactone (AHL)-dependent quorum-sensing LuxR subfamily of proteins. However, we previously reported that X. oryzae pv. oryzae does not make AHLs and does not possess a LuxI-family AHL synthase and that the Or...

Salvianolic acid A (Sal A), an important constituent of Radix Salviae Miltiorrhizae (RSM), is effective for the treatment of myocardial infarction (MI) and coronary heart disease due to its potential in the improvement of acute myocardial ischemia. However, its content is very low in RSM. So it is obvious to find a rich source of Sal A or to improve its content by conversion of other related co...