Title :
The experimental study of population-based parameter optimization algorithms on rule-based ecological modelling
Author :
Cao, Hongqing ; Recknagel, Friedrich ; Orr, Philip T.
Author_Institution :
Sch. of Earth & Environ. Sci., Univ. of Adelaide, Adelaide, SA, Australia
Abstract :
This study investigates six population-based algorithms for the parameter optimization (PO) within the hybrid methodology developed for modelling algal abundance by rule-based models. These PO algorithms include: (1) Hill Climbing (2) Simulated Annealing (3) Genetic Algorithm (4) Differential Evolution (5) Covariance Matrix Adaptation Evolution Strategy and (6) Estimation of Distribution Algorithm. The effectiveness of algorithms is tested on the Cylindrospermopsis abundance data from Wivenhoe Reservoir in Queensland (Australia). We provide a systematic analysis and comparison of different parameter optimization algorithms as well as the resulting predictive rule models.
Keywords :
covariance matrices; ecology; genetic algorithms; knowledge based systems; microorganisms; parameter estimation; simulated annealing; Australia; Cylindrospermopsis abundance data; PO algorithms; Queensland; Wivenhoe Reservoir; algal abundance modelling; covariance matrix adaptation evolution strategy; differential evolution algorithm; estimation of distribution algorithm; genetic algorithm; hill climbing algorithm; hybrid methodology; population-based parameter optimization algorithms; rule-based ecological modelling; simulated annealing algorithm; systematic analysis; Annealing; Australia; Biological system modeling; Data models; Educational institutions; Gold; Helium; ecological modelling; evolutionary algorithm; genetic programming; parameter optimization; population-based algorithm;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6252957
