Evolving agent-based models using self-adaptive complexification
Authored by Wentong Cai, Michael Wagner, Michael H Lees, Heiko Aydt
Date Published: 2015
DOI: 10.1016/j.jocs.2015.03.005
Sponsors:
Russian Scientific Foundation
Singapore National Research Foundation
Platforms:
MASON
Model Documentation:
Other Narrative
Flow charts
Model Code URLs:
Model code not found
Abstract
This paper focuses on parameter search for multi-agent based models
using evolutionary algorithms. Large numbers and variable dimensions of
parameters require an optimization method which can efficiently handle a
high dimensional search space. We are proposing the use of
complexification as it emulates the natural way of evolution by starting
with a small constrained search space and expanding it as the evolution
progresses. To further improve performance we suggest and experiment
with methods of self-adaptation to enable the algorithm to adjust its
parameters individually to the problem at hand. We examined the effects
of these methods on an EA by evolving parameters for two multi-agent
based models. (C) 2015 Elsevier B.V. All rights reserved.
Tags