TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods
Abstract
While a large number of adaptive Differential Evolution (DE) algorithms have been proposed, their Parameter Adaptation Methods (PAMs) are not well understood. We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs. The proposed TPAM simulation framework measures the ability of PAMs to track predefined target parameters, thus enabling quantitative analysis of the adaptive behavior of PAMs. We evaluate the tracking performance of PAMs of widely used five adaptive DEs (jDE, EPSDE, JADE, MDE, and SHADE) on the proposed TPAM, and show that TPAM can provide important insights on PAMs, e.g., why the PAM of SHADE performs better than that of JADE, and under what conditions the PAM of EPSDE fails at parameter adaptation.
Cite
@article{arxiv.2010.01877,
title = {TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods},
author = {Ryoji Tanabe and Alex Fukunaga},
journal= {arXiv preprint arXiv:2010.01877},
year = {2020}
}
Comments
This is an accepted version of a paper published in the proceedings of GECCO 2017