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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The main components of this algorithm are chromosomes population (individuals population), parent selection, crossover to produce new offspring, and random mutation. In this paper, continuous genetic algorithm was implemented to estimate parameters in a predator-prey model of Lotka-Volterra type. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, the algorithms can estimate these parameters well.

Original languageEnglish
Title of host publication5th International Conference and Workshop on Basic and Applied Sciences, ICOWOBAS 2015
EditorsMoh. Yasin, Sulaiman W. Harun
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735413641
DOIs
Publication statusPublished - 15 Mar 2016
Event5th International Conference and Workshop on Basic and Applied Sciences, ICOWOBAS 2015 - Surabaya, Indonesia
Duration: 16 Oct 201517 Oct 2015

Publication series

NameAIP Conference Proceedings
Volume1718
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th International Conference and Workshop on Basic and Applied Sciences, ICOWOBAS 2015
Country/TerritoryIndonesia
CitySurabaya
Period16/10/1517/10/15

Keywords

  • Lotka-Volterra type
  • continuous genetic algorithm
  • mathematical model
  • parameter estimation
  • predator-prey

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