Parameter estimation in mathematical models using the real coded genetic algorithms

Tutkun, Nedim

doi:10.1016/j.eswa.2008.01.060

Cited by 39 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study showed that the recombination searching strategy applied by this method was robust to measurement noise in the experimental data. Similarly, Particle Swarm Optimization (PSO) [19] and Genetic Algorithm (GA) [20] methods were also used to estimate the parameters in biological systems, which showed promising results [21], [22]. More recently, evolutionary-based meta-heuristics methods have received remarkable attentions [1], [3], [23].…”

Section: Introductionmentioning

confidence: 99%

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

et al. 2013

View full text Add to dashboard Cite

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The GA is carried out according to the following steps: 1) Creating a population with 100 individuals; 2) evaluating the fitness for each individual; 3) selecting individuals according to their fitness values and building a temporary population; 4) implementing single point real crossover with the probability of 0.6 on the temporary population; 5) mutating the current population with the probability of 0.05; 6) repeating the steps 2, 3, 4, 5 and 6 until the number of the generations is met. It should be pointed out that the specific parameters in GA are referenced from Tutkun (2009). …”

Section: Genetic Algorithmmentioning

confidence: 99%

Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

2010

View full text Add to dashboard Cite

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

show abstract

“…Little and Lodish (1981) made a commentary on judgment-based marketing decision models. As a note, Tutkun (2009) suggested using the real coded genetic algorithm approach for parameter estimation in mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Optimizing time limits for maximum sales response in Internet shopping promotions

Chiang

Lin

Chin

2011

Expert Systems with Applications

View full text Add to dashboard Cite

Parameter estimation in mathematical models using the real coded genetic algorithms

Cited by 39 publications

References 6 publications

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

Optimizing time limits for maximum sales response in Internet shopping promotions

Contact Info

Product

Resources

About