by van Hemert, J, Van Hoyweghen, C, E Lukschandl and K Verbeeck
Abstract:
The optimization of dynamic environments has proved to be a difficult area for Evolutionary Algorithms. As standard haploid populations find it difficult to track a moving target, diffKerent schemes have been suggested to improve the situation. We study a novel approach by making use of a meta learner which tries to predict the next state of the environment, i.e. the next value of the goal the individuals have to achieve, by making use of the accumulated knowledge from past performance.
Reference:
A "Futurist" approach to dynamic environments (van Hemert, J, Van Hoyweghen, C, E Lukschandl and K Verbeeck), In Genetic and Evolutionary Computation (J Branke, Th. Bäck, eds.), Morgan Kaufmann Publishers, volume 3, 2001.
Bibtex Entry:
@article{HHLV2001,
_day = {17},
abstract = {The optimization of dynamic environments has proved to be a difficult area for Evolutionary Algorithms. As standard haploid populations find it difficult to track a moving target, diffKerent schemes have been suggested to improve the situation. We study a novel approach by making use of a meta learner which tries to predict the next state of the environment, i.e. the next value of the goal the individuals have to achieve, by making use of the accumulated knowledge from past performance.},
author = {van Hemert, J and Van Hoyweghen, C and E Lukschandl and K Verbeeck},
date-added = {2008-08-18 12:44:11 +0100},
date-modified = {2009-01-22 21:46:14 +0000},
editor = {J Branke and Th. B{\"a}ck},
journal = {Genetic and Evolutionary Computation},
keywords = {dynamic problems},
pages = {35--38},
pdf = {http://www.vanhemert.co.uk/publications/gecco2001.A_Futurist_Approach_to_Dynamic_Environments.pdf},
ps.gz = {http://www.vanhemert.co.uk/publications/gecco2001.A_Futurist_Approach_to_Dynamic_Environments.ps.gz},
publisher = {Morgan Kaufmann Publishers},
title = {A ``Futurist'' approach to dynamic environments},
volume = {3},
year = {2001}}