Parameter estimation of a predator-prey model using a genetic algorithm

Restrepo, Jackelyne Gomez; Vélez, Carlos M.

doi:10.1109/andescon.2010.5633365

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…In this section, we apply our method to two case studies [17,22] taken from the literature to show its potential. After presenting the studies and their results, we will display our results and discuss them.…”

Section: Case Studiesmentioning

confidence: 99%

“…4.2 Case study 2: a prey-predator model for lynx and hares [22] In 2010, Restrepo and Sánchez [22] published a paper describing a genetic algorithm, which aimed at estimating the best parameters for prey-predator models.…”

Section: Case Studiesmentioning

confidence: 99%

“…where t is time, P and D are the two time-dependent variables representing the quantity of individuals in each group: P (t) for prey and D(t) for predators; a, b, c, d are positive constants, a and c indicating the birth rate of prey and death rate of predators respectively, and b and d representing the rates of predation and reproduction of predators. Note that even if the model is a standard way to describe, on rst approximation, such an interaction between two populations, its simplicity might make it imprecise which is why other, more complex models are studied in [22]. The best values for parameters (a, b, c, d) w.r.t.…”

Section: Case Studiesmentioning

confidence: 99%

“…The best values for parameters (a, b, c, d) w.r.t. experimental data are given in [22]: (a, b, c, d) = (0.55, 0.027, 0.83, 0.026).…”

Section: Case Studiesmentioning

confidence: 99%

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End-to-End Statistical Model Checking for Parametric ODE Models

Julien

Cantin

Delahaye

2022

Quantitative Evaluation of Systems

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We propose a simulation-based technique for the verication of structural parameters in Ordinary Dierential Equations. This technique is an adaptation of Statistical Model Checking, often used to verify the validity of biological models, to the setting of Ordinary Dierential Equations systems. The aim of our technique is to search the parameter space for the parameter values that induce solutions that best t experimental data under variability, with any metrics of choice. To do so, we discretize the parameter space and use statistical model checking to grade each individual parameter value w.r.t experimental data. Contrary to other existing methods, we provide statistical guarantees regarding our results that take into account the unavoidable approximation errors introduced through the numerical resolution of the ODE system performed while simulating. In order to show the potential of our technique, we present its application to two case studies taken from the literature, one relative to the growth of a jellysh population, and another concerning a prey-predator model.

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Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

“…The best values for parameters (a, b, c, d) w.r.t. experimental data are given in [22]: (a, b, c, d) = (0.55, 0.027, 0.83, 0.026).…”

Section: Case Studiesmentioning

confidence: 99%

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End-to-End Statistical Model Checking for Parametric ODE Models

Julien

Cantin

Delahaye

2022

Quantitative Evaluation of Systems

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“…Sejarah awal tentang model prey-predator dapat dilihat pada Berryman dkk [10]. Beberapa penelitian yang menerapkan algoritma genetika dalam melakukan estimasi parameter untuk model prey-predator dapat dilihat di [11][12][13]. Di artikel ini akan dipelajari estimasi parameter dengan menggunakan data anjing hutan (coyote) dan kelinci besar (jackrabbit) yang masing-masing adalah predator dan prey di Southwest Prescott, Arizona [14].…”

Section: Introductionunclassified

Implementasi algoritma genetika dalam mengestimasi kepadatan populasi jackrabbit dan coyote

Savitri

Hidajati

Panigoro³

2022

Jambura J. Biomath.

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This article studies about the parameter estimation using genetic algorithm for a Lotka-Volterra prey-predator model. The secondary data consist of the density of jackrabbit as prey and coyote as predator in Southwest Presscott–Arizona are used. As results, the Mean Absolute Percentage Error (MAPE) are computed to compare the results of parameter estimation and the real data. We have shown that MAPE for jackrabbit and coyote respectively given by 7.75424% and 7.95283%. This results show that the parameter estimation with genetic algorithm using Lotka-Volterra model is passably. Furthermore, some numerical simulations are portrayed to show each population density for the next 100 years.

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An implementation of continuous genetic algorithm in parameter estimation of predator-prey model

Windarto

2016

AIP Conference Proceedings

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Parameter estimation of a predator-prey model using a genetic algorithm

Cited by 3 publications

References 12 publications

End-to-End Statistical Model Checking for Parametric ODE Models

End-to-End Statistical Model Checking for Parametric ODE Models

Implementasi algoritma genetika dalam mengestimasi kepadatan populasi jackrabbit dan coyote

An implementation of continuous genetic algorithm in parameter estimation of predator-prey model

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