Evolutionary Computation in System Identification: Review and Recommendations

Two of the steps in system identification are model structure selection and parameter estimation. In model structure selection, several model structures are evaluated and selected. Because the evaluation of all possible model structures during selection and estimation of the parameters requires a lot of time, a rigorous method in which these tasks can be simplified is usually preferred. This paper reviews cumulatively some of the methods that have been tried since the past 40 years. Among the methods, evolutionary computation is known to be the most recent one and hereby being reviewed in more detail, including what advantages the method contains and how it is specifically implemented. At the end of the paper, some recommendations are provided on how evolutionary computation can be utilized in a more effective way. In short, these are by modifying the search strategy and simplifying the procedure based on problem a priori knowledge

PEPA'd Oysters: Converting Dynamic Energy Budget Models to Bio-PEPA, illustrated by a Pacific oyster case study

We present a Bio-PEPA (Biochemical-Performance Evaluation Process Algebra) computational model for the Pacific oyster, derived from a DEB (Dynamic Energy Budget) mathematical model. Experience with this specific model allows us to propose a generic scheme for translation between the widely-used DEB theory and Bio-PEPA. The benefits of translation are that a range of novel analysis tools become available, therefore improving the potential to understand complex biological phenomena at a systems level. This work also provides a link between biology, mathematics and computer science: such interlinking of disciplines is the core of the systems approach to biology

Optimisation of Process Algebra Models Using Evolutionary Computation

We propose that process algebras and evolutionary algorithms have complementary strengths for developing models of complex systems. Evolutionary algorithms are powerful methods for finding solutions to optimisation problems with large search spaces but require an accurately defined fitness function to provide valid results. Process algebras are an effective method for defining models of complex interacting processes, but tuning parameters to allow model outputs to match experimental data can be difficult. Defining models in the first place can also be problematic. Our long term goal is to build a framework to synthesise process algebra models. Here we present a first step in that development: combining process algebra with an evolutionary approach to fine tune the numeric parameters of predefined models. The Evolving Process Algebra (EPA) framework is demonstrated through examples from epidemiology and computer science