HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation

Pătraşcu, Monica; Stancu, Alexandra Florentina; Pop, Florin

doi:10.1007/s00500-014-1401-y

Cited by 22 publications

(7 citation statements)

References 31 publications

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“…Each individual in nature stores genetic information in a chromosome. For the GA chromosomes can be represented by bit arrays so that continuous variables are approximated with a binary decomposition [28]. For a binary representation of a parameter with n V alues values, the number of bits k needs to be chosen so that the condition 2 k ≥ n V alues is met.…”

Section: ) Genetic Algorithmmentioning

confidence: 99%

Efficient Simulation Based Calibration of Automated Driving Functions Based on Sensitivity Based Optimization

Fraikin

Funk

Frey

et al. 2020

IEEE Open J. Intell. Transp. Syst.

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Section: ) Genetic Algorithmmentioning

confidence: 99%

Efficient Simulation Based Calibration of Automated Driving Functions Based on Sensitivity Based Optimization

Fraikin

Funk

Frey

et al. 2020

IEEE Open J. Intell. Transp. Syst.

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“…Genetic algorithm (GA), an optimization technique that simulates the concept of "survival of the fittest", is a computational model developed, based on the adaptability of organisms to the environment and the principle of natural selection [29,30]. Since the search direction or scope relies on probabilistic differences across generations, GA is an effective optimization algorithm that is less likely to get stuck at a local minimum.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Case Study: On Objective Functions for the Peak Flow Calibration and for the Representative Parameter Estimation of the Basin

Kim

Joo

et al. 2018

Water

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The objective function is usually used for verification of the optimization process between observed and simulated flows for the parameter estimation of rainfall-runoff model. However, it does not focus on peak flow and on representative parameter for various rain storm events of the basin, but it can estimate the optimal parameters by minimizing the overall error of observed and simulated flows. Therefore, the aim of this study is to suggest the objective functions that can fit peak flow in hydrograph and estimate the representative parameter of the basin for the events. The Streamflow Synthesis And Reservoir Regulation (SSARR) model was employed to perform flood runoff simulation for the Mihocheon stream basin in Geum River, Korea. Optimization was conducted using three calibration methods: genetic algorithm, pattern search, and the Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA). Two objective functions of the Sum of Squared of Residual (SSR) and the Weighted Sum of Squared of Residual (WSSR) suggested in this study for peak flow optimization were applied. Since the parameters estimated using a single rain storm event do not represent the parameters for various rain storms in the basin, we used the representative objective function that can minimize the sum of objective functions of the events. Six rain storm events were used for the parameter estimation. Four events were used for the calibration and the other two for validation; then, the results by SSR and WSSR were compared. Flow runoff simulation was carried out based on the proposed objective functions, and the objective function of WSSR was found to be more useful than that of SSR in the simulation of peak flow runoff. Representative parameters that minimize the objective function for each of the four rain storm events were estimated. The calibrated observed and simulated flow runoff hydrographs obtained from applying the estimated representative parameters to two different rain storm events were better than those retrieved from parameters estimated using a single rain storm event. The results of this study demonstrated that WSSR is adequate in peak flow simulation, that is, the estimation of peak flood runoff. In addition, representative parameters can be applied to a flow runoff simulation for rain storm events that were not involved in parameter estimation.

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“…Genetic algorithm (GA) [5] is a metaheuristic inspired by the process of natural selection and belongs to the larger class of evolutionary algorithms [6]. It was recommended as an important optimizer for nonlinear PID control systems [7].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based particle swarm optimization for PID controller tuning

Chen

Omidvar

Azad

et al. 2017

2017 IEEE Congress on Evolutionary Computation (CEC)

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HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation

Cited by 22 publications

References 31 publications

Efficient Simulation Based Calibration of Automated Driving Functions Based on Sensitivity Based Optimization

Efficient Simulation Based Calibration of Automated Driving Functions Based on Sensitivity Based Optimization

Case Study: On Objective Functions for the Peak Flow Calibration and for the Representative Parameter Estimation of the Basin

Knowledge-based particle swarm optimization for PID controller tuning

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