Multi-objective analysis of computational models

TL;DR

This paper reviews multi-objective evolutionary algorithms for analyzing complex computational models, providing a unifying framework and illustrating its use through examples like a flapping-wing robot and a neurocomputational brain model.

Contribution

It introduces a unifying framework for multi-objective analysis of models and demonstrates its application with two detailed case studies.

Findings

Identified critical parameters of the flapping-wing robot.

Revealed key features of the Basal Ganglia model.

Showed how trade-off solutions aid understanding of model behavior.

Abstract

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.