Publications

@article{CEREPNALKOSKI2012136,

title = {The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems},

author = {Darko \v{C}erepnalkoski and Katerina Ta\v{s}kova and Ljup\v{c}o Todorovski and Nata\v{s}a Atanasova and Sa\v{s}o D\v{z}eroski},

url = {https://www.sciencedirect.com/science/article/pii/S0304380012002724},

doi = {https://doi.org/10.1016/j.ecolmodel.2012.06.001},

issn = {0304-3800},

year  = {2012},

date = {2012-01-01},

journal = {Ecological Modelling},

volume = {245},

pages = {136-165},

abstract = {Modeling dynamical systems involves two subtasks: structure identification and parameter estimation. ProBMoT is a tool for automated modeling of dynamical systems that addresses both tasks simultaneously. It takes into account domain knowledge formalized as templates for components of the process-based models: entities and processes. Taking a conceptual model of the system, the library of domain knowledge, and measurements of a particular dynamical system, it identifies both the structure and numerical parameters of the appropriate process-based model. ProBMoT has two main components corresponding to the two subtasks of modeling. The first component is concerned with generating candidate model structures that adhere to the conceptual model specified as input. The second subsystem uses the measured data to find suitable values for the constant parameters of a given model by using parameter estimation methods. ProBMoT uses model error to rank model structures and select the one that fits measured data best. In this paper, we investigate the influence of the selection of the parameter estimation methods on the structure identification. We consider one local (derivative-based) and one global (meta-heuristic) parameter estimation method. As opposed to other comparative studies of parameter estimation methods that focus on identifying parameters of a single model structure, we compare the parameter estimation methods in the context of repetitive parameter estimation for a number of candidate model structures. The results confirm the superiority of the global optimization methods over the local ones in the context of structure identification.},

note = {7th European Conference on Ecological Modelling (ECEM)},

keywords = {Aquatic ecosystems, Dynamical systems, Equation discovery, Meta-heuristic optimization, Parameter estimation, Process-based modeling},

pubstate = {published},

tppubtype = {article}

}

@article{TASHKOVA201236,

title = {Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization},

author = {Katerina Taskova and Jurij \v{S}ilc and Nata\v{s}a Atanasova and Sa\v{s}o D\v{z}eroski},

url = {https://www.sciencedirect.com/science/article/pii/S0304380011005795},

doi = {https://doi.org/10.1016/j.ecolmodel.2011.11.029},

issn = {0304-3800},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {Ecological Modelling},

volume = {226},

pages = {36-61},

abstract = {Parameter estimation in dynamic models of ecosystems is essentially an optimization task. Due to the characteristics of ecosystems and typical models thereof, such as non-linearity, high dimensionality, and low quantity and quality of observed data, this optimization task can be very hard for traditional (derivative-based or local) optimization methods. This calls for the use of advanced meta-heuristic approaches, such as evolutionary or swarm-based methods. In this paper, we conduct an empirical comparison of four meta-heuristic optimization methods, and one local optimization method as a baseline, on a representative task of parameter estimation in a nonlinear dynamic model of an aquatic ecosystem. The five methods compared are the differential ant-stigmergy algorithm (DASA) and its continuous variant (CDASA), particle swarm optimization (PSO), differential evolution (DE) and algorithm 717 (A717). We use synthetic data, both without and with different levels of noise, as well as real measurements from Lake Bled. We also consider two different simulation approaches: teacher forcing, which makes supervised predictions one (small) time step ahead, and full (multistep) simulation, which makes predictions based on the history predictions for longer time periods. The meta-heuristic global optimization methods for parameter estimation are clearly superior and should be preferred over local optimization methods. While the differences in performance between the different methods within the class of meta-heuristics are not significant across all conditions, differential evolution yields the best results in terms of quality of the reconstructed system dynamics as well as speed of convergence. While the use of teacher forcing simulation makes parameter estimation much faster, the use of full simulation produces much better parameter estimates from real measured data.},

keywords = {Aquatic ecosystems, Least-squares estimation, Meta-heuristic optimization, Ordinary differential equations, Parameter estimation},

pubstate = {published},

tppubtype = {article}

}

@article{Taskova2011Parameter,

title = {Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis},

author = {Katerina Taskova and Peter Koro\v{s}ec and Jurij \v{S}ilc and Sa\v{s}o D\v{z}eroski},

doi = {10.1186/1752-0509-5-159},

year  = {2011},

date = {2011-10-11},

journal = {BMC Systems Biology},

volume = {5},

issue = {1},

pages = {1752-0509},

keywords = {machine learning, Parameter estimation},

pubstate = {published},

tppubtype = {article}

}