Multi-objective analysis of computational models
This work addresses the difficulty in understanding emergent behaviors in complex computational models for researchers and practitioners, though it is incremental as it reviews and unifies existing methods.
The paper tackles the challenge of analyzing complex computational models by proposing a multi-objective evolutionary framework to identify trade-off solutions, enabling data mining to uncover model features and regularities, as demonstrated through examples including a flapping-wing robot and a neurocomputational model.
Computational models are of increasing complexity and their behavior may in particular emerge from the interaction of different parts. Studying such models becomes then more and more difficult and there is a need for methods and tools supporting this process. Multi-objective evolutionary algorithms generate a set of trade-off solutions instead of a single optimal solution. The availability of a set of solutions that have the specificity to be optimal relative to carefully chosen objectives allows to perform data mining in order to better understand model features and regularities. We review the corresponding work, propose a unifying framework, and highlight its potential use. Typical questions that such a methodology allows to address are the following: what are the most critical parameters of the model? What are the relations between the parameters and the objectives? What are the typical behaviors of the model? Two examples are provided to illustrate the capabilities of the methodology. The features of a flapping-wing robot are thus evaluated to find out its speed-energy relation, together with the criticality of its parameters. A neurocomputational model of the Basal Ganglia brain nuclei is then considered and its most salient features according to this methodology are presented and discussed.