Using multiobjective evolutionary algorithms to assess biological simulation models

We introduce an important general Multiobjective Evolutionary Algorithm (MOEA) application - assessment of mechanistic simulation models in biology. These models are often developed to investigate the processes underlying biological phenomena. The proposed model structure must be assessed to reveal if it adequately describes the phenomenon. Objective functions are defined to measure how well the simulations reproduce specific phenomenon features. They may be continuous or binary-valued, e.g. constraints, depending on the quality and quantity of phenomenon data. Assessment requires estimating and exploring the model's Pareto frontier. To illustrate the problem, we assess a model of shoot growth in pine trees using an elitist MOEA based on Nondominated Sorting in Genetic Algorithms. The algorithm uses the partition induced on the parameter space by the binary-valued objectives. Repeating the assessment with tighter constraints revealed model structure improvements required for a more accurate simulation of the biological phenomenon.