REVAC

Project Index

Fri, 18 May 2007 18:46:24 +0200

The REVAC method provides an information theoretic measure on how sensitive a parameter is to the choice of its value. This can be used to estimate the relevance of parameters, to choose between different possible sets of parameters, and to allocate resources to the calibration of relevant parameters.

REVAC

Measuring relevance by Shannon entropy

volker — Thu, 10 May 2007 15:25:35 +0200

Because REVAC is implemented as a sequence of distributions with slowly decreasing Shannon entropy we can use the Shannon entropy of these distributions to estimate the minimum amount of information needed to reach a target performance level. We can also measure how this information is distributed over the parameters, resulting in a straightforward measure for parameter relevance.

REVAC

Algorithm details

volker — Thu, 10 May 2007 15:10:48 +0200

REVAC starts from an initial table TeX Embedding failed! that was drawn from the uniform distribution over TeX Embedding failed!. The update process that creates a new table TeX Embedding failed! from a given TeX Embedding failed! can be described from both the horizontal and the vertical perspective. Looking at Table from the horizontal perspective we can identify two basic steps:

REVAC

Demonstration

volker — Thu, 10 May 2007 03:29:29 +0200

This demonstration is based on the file calibrate_ga.m in the software bundle on the software page. The function calibrates a generational genetic algorithm (GA) on one of four test functions: sphere (f1), saddle (f2), step (f3), and Schaffer's f6 (f6).

REVAC

Publications

volker — Tue, 08 May 2007 01:20:51 +0200

Volker Nannen, S. K. Smit, and A. E. Eiben, Costs and Benefits of Tuning Parameters of Evolutionary Algorithms, Proceedings of the 20th Conference on Parallel Problem Solving from Nature, 2008. [pdf]

REVAC

Software

volker — Tue, 08 May 2007 01:19:28 +0200

A zip file with the matlab code used for the IJCAI-07 paper can be downloaded here. After unpacking you have a directory revac_demo/ with the following files and directories:

REVAC

Algorithm

volker — Tue, 08 May 2007 01:18:53 +0200

REVAC uses information theory to measure parameter relevance. Instead of estimating the performance of an EA for specific parameter values or ranges of values, REVAC estimates the expected performance when parameter values are chosen from specific probability density distributions.

REVAC

calibrate_ga.m A function that allows you to test REVAC on a genetic algorithm