Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm

This paper presents a new approach, based on the principles of fuzzy logic and Genetic Algorithm (GA) for selection of optimal process parameters in Abrasive Water Jet (AWJ) cutting of granite to any predetermined depth, using multi-criteria optimization technique. The proposed approach suggests the best combination of process parameters such as water jet pressure, jet traverse rate and abrasive flow rate for cutting granite material to any predetermined depth. GA, in combination with the model built based on fuzzy approach, generates several sets of process parameters satisfying the objective of achieving the desired depth of cut. These sets of parameters are subjected to multi-criteria optimization procedure which suggests a set of process parameters that can minimize the cost of production by increasing the rate of production and reducing the consumption of abrasives, maintaining the desired depth of cut within the specified limits. The proposed approach is validated with suitable experiments conducted on Paradiso granite.

Patients with Type I Diabetes may develop some complications during their lifetime as a result of impaired metabolic control; among others, some of these common complications concerns microvascular function. The possibility of evaluating the cutaneous microcirculation is considered an important translational model because it could offer a important opportunity to non-invasive evaluation of diabetes-related vascular disorders.In our previous works we demonstrated the alteration occurring in the vascular system reflect themselves in the shape of the peripheral waveform. In particular, we demonstrated the decomposition of the waveform as a sum of Gaussian curves provides a set of parameters that can be fed into a classifier that successfully discriminate between younger and older subjects.In the present work, we investigated the application of a different decomposition model, based on a sum of exponential pulses, for the analysis of microvascular alterations correlated with the presence of Type I Diabetes.The processing pipeline follow the basic architecture of our previous work: a pre-processing step segments each pulsation of the input signal and the removes the long-term trend. Afterward, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximate each single cardiac cycle.Each vector of model parameters constitutes a compact representation of the pulse waveform; thus, any structural difference in the pulse waveform between controls and patient produces a corresponding alteration of model parameters.Finally, an example classifier is used to explore the feasibility of discriminating pathological and normal subjects by looking only at the peripheral waveform.

This paper presents results from the application of the genetic algorithm (GA) technique to the design optimization of hydrofoil and marine propeller incorporating potential based boundary element method (BEM). Although, larger population size as implemented by simple genetic algorithm (SGA) could find the optimal individual after a fewer number of generations than smaller population size, it is penalized by a longer amount of time to evaluate fitness in every generation. An investigation is, therefore, conducted in this research to implement micro genetic algorithm (μGA) with a very small population, and with simple genetic parameters, in order to achieve faster convergence to better solution from generation to generation. The technique is applied here to optimize hydrofoils of different plan forms, e.g., rectangular, elliptical, trapezoidal etc. Firstly, the hydrofoil design parameters, such as, angle of incidence, maximum thickness and camber ratios, aspect ratio, taper ratio, angle of sweep etc. are initialized randomly and the generated hydrofoil is analyzed by potential based boundary element method. GA then updates the design parameters over generation after generation and finally, finds an improved hydrofoil of maximum lift-drag ratio or minimum drag coefficient satisfying some design constraints. An improved blade or hydrofoil section is also designed by GA satisfying some design constraints. Finally, the technique is applied to the optimum design of marine propeller. In this study, μGA is found useful and prospective tool for the design optimization of hydrofoil and marine propeller due to its faster convergence. Keywords: Genetic algorithm, boundary element method, hydrofoil, propeller, design optimization  doi: 10.3329/jname.v1i1.2038 Journal of Naval Architecture and Marine Engineering 1(2004) 47-61

The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms that compute a proximity given as input a pair of concept labels. The algorithms depends on a semantic graph, provided as RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF graph). The current research objective is to automatically tune the weights for a given graph in order to increase the proximity quality. The quality of a semantic relatedness method is usually measured against a benchmark data set. The results produced by the method are compared with those on the benchmark using the Spearman's rank coefficient. This methodology works the other way round and uses this coefficient to tune the proximity weights. The tuning process is controlled by a genetic algorithm using the Spearman's rank coefficient as the fitness function. The genetic algorithm has its own set of parameters which also need to be tuned. Bootstrapping is based on a statistical method for generating samples that is used in this methodology to enable a large number of repetitions of the genetic algorithm, exploring the results of alternative parameter settings. This approach raises several technical challenges due to its computational complexity. This paper provides details on the techniques used to speedup this process. The proposed approach was validated with the WordNet 2.0 and the WordSim-353 data set. Several ranges of parameters values were tested and the obtained results are better than the state of the art methods for computing semantic relatedness using the WordNet 2.0, with the advantage of not requiring any domain knowledge of the ontological graph.

In this paper, we study the resource management problem in Direct Sequence-Wideband Code Division Multiple Access (DS-WCDMA) systems. The control variables, transmission power, and transmission rate for resource management are considered. Three meta-heuristic techniques, Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS), are utilized to solve this optimization problem. Also, a nonlinear programming technique, Generalized Reduced Gradient (GRG) method, is adopted to compare with the three meta-heuristic techniques. Two approaches, the single objective approach and the multiobjective approach, are used in the simulation. The results obtained by GA, SA, TS, and GRG are compared in a single objective approach. In a multiobjective approach, Multiobjective Genetic Algorithm (MOGA) is employed to compare with the other two well-known multiobjective evolutionary algorithms (MOEAs), Pareto Archived Evolution Strategy (PAES) and Micro-Genetic Algorithms (MICRO-GA). Two scenarios, scenario (a): 25 users and scenario (b): 50 users, are considered for both approaches. The simulation results of a single objective approach show that GA outperforms SA, TS, and GRG in the two scenarios. Also, the simulation results of a multiobjective approach show that MOGA outperforms PAES and MICRO-GA, and obtains nondominated trade-off solutions with better convergence and diversity between trade minimization of total power and maximization of total rate in two scenarios.

In this paper, multi–stage supply chain systems (SCSs) controlled by kanban system are appraised a new simulation metaheuristics approach. In the kanban system, decision making is based on determination of batch size for each kanban. This paper simulates supply chain system regarding the costs under just–in–time (JIT) production philosophy. Since the adopted model is of backward type, the desired output is given in order to find the parameters and/or the structure of the model producing the output. This backward problem is non–analytic and often seems to be even more complex than the forward one. This paper applies genetic algorithm (GA) and variable neighbourhood search (VNS) to optimise the simulation model. A simple real–coded GA and VNS is presented and used to change the simulation model parameters. With each new set of parameters, a simulation run is performed. From the statistics gathered by running the simulation, a goal function is constructed to measure the quality of these parameters. GA and VNS and GA–VNS successfully provide a parameter set to demonstrate its capability to solve such difficult backward problems even in the area of complex simulation model optimisation specially when there is no prior knowledge of simulation model behaviour.

Land surface models involve a large number of interdependent parameters that affect the physics of how surface energy fluxes are partitioned between latent heat, sensible heat, net radiative, and ground heat fluxes. The goal of an optimal parameter and uncertainty analysis of a land surface model is to identify a range of parameter sets that enable model predictions to be bounded within observational uncertainties. Here we apply Bayesian stochastic inversion (BSI) using very fast simulated annealing (VFSA) to identify parameter sets of the Chameleon surface model (CHASM) land surface model that are consistent with the uncertainty limits ascribed to a high‐quality data set collected from Cabauw, Netherlands. These results are compared to the parameter sets obtained through the multicriteria (MC) approach. All analyses evaluate model performance against daily and monthly mean observations of sensible, latent, and ground heat fluxes. BSI and MC identify similar “best fit” model parameter sets that improve CHASM performance over default parameter settings. The three most important CHASM parameters at Cabauw are minimum stomatal resistance, vegetation roughness length, and vegetation fraction cover. BSI is based on a Bayesian inference model such that that it expresses uncertainty in terms of a posterior probability density function, different moments of which provide information about parameter means and covariances. Although MC gives a range of possible optimal parameters through the concept of a Pareto set, we found that these ranges did not provide a consistent or representative view of the uncertainty within the observational data. The BSI algorithm in the current study is particularly efficient in that it only requires about double the number of model evaluations than the MC algorithm. This is a substantial saving over other more accurate methods to evaluate uncertainty such as the Metropolis/Gibbs' sampler that requires at least 40 times more computations than the BSI algorithm to obtain similar results.

To tackle several combinatorial optimization problems, genetic algorithms were frequently used as powerful evolutionary methods. However, performance of these algorithms is heavily dependent on finding appropriate parameters and operators. This phenomenon is known as the parameter tuning problem. The latter falls into the category of hard problems. Therefore, we develop a heuristic approach for effective parameter tuning of genetic algorithms. We also focus on determining the most appropriate genetic operators. Our approach detects the promising available set of parameters. For the practical demonstration, we tackle the sum coloring of graphs. Results show the practical performance of the proposed method to enhance the genetic algorithm.

As part of the comprehensive theme of the relationships between dynamic systems and computational theories, a very active area of research has been the relationships between the generic dynamic behavior of Cellular Automata (CA) and their computational abilities. Various investigations have been carried out on the computational power of CA, with concentrated efforts in the study of one-dimensional CA and their computational abilities. One of the approaches is the use of Genetic Algorithms (GA) to look for CA with a predefined computational behavior. A set of parameters which we have previously shown to be effective in helping forecast CA dynamic behavior, are used here as an auxiliary metric to guide the GA search. To this end, we modified selection, mutation and crossover of a GA, so as to incorporate the heuristic, and obtained very effective results in the evolution search for CA that can solve the so-called synchronization task.

Soft computing techniques, e.g. neural networks, fuzzy inference, evolutionary computation, and chaos theory, have been applied to a wide variety of control systems in industry because of their control capability and flexibility. They are also powerful to handle the complicated mechatronics systems with various nonlinearities which are difficult to be modeled by mathematical formulas. This paper presents a novel evolutionary algorithm for the identification of unknown structured mechatronics systems using genetic algorithms (GA), where the optimal system mathematical structure and its set of parameters can be determined by means of the optimization ability of GA. The effectiveness of the proposed identification can be verified by experiments using the typical mechanical systems with a velocity controller.

An accurate forecast of runoff is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood. Due to the highly dimension and nonlinear, the calibration of hydrological model become very complex, so the unique “best” parameter set can not be obtained easily. In this study, an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm(SCEM-UA) is presented, which is well suited to infer the posterior distribution of hydrologic model parameters. This algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Therefore, SCEM-UA is applied in this study to calibrate a lumped Xinanjiang hydrological model having 16 parameters. Three types of data were used for Xinanjiang model: rainfall, evaporation and discharge. One years’ data were used for calibration and one years’ data were used for testing. The criterion used to measure the fitness of the calculated against the observed discharges was the Deterministic Coefficient (DC) and Root Mean Square Error(RMSE). The calibration processes included first of all defining the feasible domains of the model parameters, and initialize the parameters in the feasible domains, then the model parameters were iteratively evaluated and updated, until the terminal condition was satisfied. In order to test the efficiency of the SCEM-UA, Genetic algorithm (GA) is also employed for comparison. The results showed that both calibration and testing results are satisfactory: the DC values of SCEM-UA for the calibration period is 0.79, which is much higher than that of GA, 0.73, and the DC for the testing period is 0.81, which is also better than GA, the same as the RMSE. Visual examinations shows in the high peak flood event, the simulated runoff by SCEM-UA is much better than that by GA.

Poor temperature properties of LSAW (Leaky Surface Acoustic Wave) devices are one of the principal problems of applying these devices in mobile communication systems. The TCF (temperature coefficient of frequency) of BAW (Bulk Acoustic Wave) filters, for example, are at least twice better compared to LSAW devices, so LSAW filters on 36-48 LiTaO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> should have much steeper transition bands for the same specification requirements. This causes increase of the LSAW filter IL (insertion loss) and cost due to bigger die size and inferior yield. The problem of improving temperature stability of SAW devices has been discussed in many papers for years. Many possible solutions were described in [1]. Currently, there are two main approaches to improve LSAW filters TCF [2], [3]. First one is based on deposition of a thin dielectric layer having positive TCF onto the filter working surface. The second approach uses bonding of a thin piezoelectric wafer onto a thermo stable carrier. Both methods can improve filter TCF by changing the effective thermo expansion coefficient and LSAW velocity. Unfortunately, both methods create new problems such as increased cost and deteriorating performance and/or reliability. In this work we propose a novel approach to improve TCF. It is based on changing the device impedance over the working temperature range and does not require a manufacturing technology change. We call this approach "Design over Temperature" (DOT). The idea is to design a filter with a special shape of impedance, improving the passband edges for varying temperatures (improving low edge under "cold" conditions and high edge- under "hot"). This new approach is based on two components: -model parameters (COM (coupling- of- modes) model in our case) are characterized not only for ambient temperature 'ta' but for edges of the operational temperature range (tmin=-30°C and tmax=+85°C) too; -the design optimization process is organized for three sets of model parameters in parallel relative to full temperature range of the filter specification requirements. The goal of the optimization process is to improve the worst from three performances out of "cold", "normal conditions" and "hot" and not just the room temperature performance for every specification requirement. First, we have extracted COM parameters for our fabrication process for three different temperatures: t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> , t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> , t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> and described temperature dependencies for all of them. The large difference in TCF for left and right frequency response slopes is trivially demonstrated using this data. Second, we setup a single optimization process for three different sets of model parameters. We have designed an RX filter for PCS (and UMTS Band 2) application based on the proposed approach. This filter has better IL over working temperature range, steeper left transition band and twice better effective TCF than filters designed by conventional design process.

The application of antenna array is an effective way to improve the gain and directivity of ultra-wide band (UWB) antenna and reduce the side lobe. It is different from the traditional narrowband antenna based on frequency domain that the finite-difference time-domain method (FDTD) was used to make simulated analysis of a UWB TEM horn antenna in time domain. According to the principle of superposition, the information of time-domain radiated field of a 9-elements linear TEM horn array was then acquired and the universal characteristic of time-domain UWB antenna array was discussed. Combined with micro genetic algorithm (MGA), the energy pattern of linear array is optimized and a narrow beam-width is available, while the maximal side-lobe level of the array is limited.

Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems

The present paper attempts to develop order reduction methods where the suggested reduction model consists of two phases.First, full order system is expanded by Hermite polynomials, then a set of parameters in a fixed structure are determined, whose values define the reduced order system.The values are obtained using Genetic Algorithm (GA) by minimizing the errors between the l first coefficients of Hermite polynomials expansion of full and reduced systems.To satisfy the stability, Routh criterion is used as constraints in optimization problem.To present the ability of the proposed method, three test systems are reduced.The results obtained are compared with other existing techniques.The results obtained show the accuracy and efficiency of the proposed method.