Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problems. In this paper, we consider the behavior of the classical (1+1) Evolutionary Algorithm for linear functions under linear constraint. We show tight bounds in the case where both the objective function and the constraint is given by the OneMax function and present upper bounds as well as lower bounds for the general case. Furthermore, we also consider the LeadingOnes fitness function.
%0 Conference Paper
%1 DBLP:conf/foga/0001KLNS17
%A Friedrich, Tobias
%A Kötzing, Timo
%A Lagodzinski, J. A. Gregor
%A Neumann, Frank
%A Schirneck, Martin
%B Foundations of Genetic Algorithms (FOGA)
%D 2017
%I ACM Press
%K testing typo3
%P 45-54
%T Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints
%X Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problems. In this paper, we consider the behavior of the classical (1+1) Evolutionary Algorithm for linear functions under linear constraint. We show tight bounds in the case where both the objective function and the constraint is given by the OneMax function and present upper bounds as well as lower bounds for the general case. Furthermore, we also consider the LeadingOnes fitness function.
@inproceedings{DBLP:conf/foga/0001KLNS17,
abstract = {Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problems. In this paper, we consider the behavior of the classical (1+1) Evolutionary Algorithm for linear functions under linear constraint. We show tight bounds in the case where both the objective function and the constraint is given by the OneMax function and present upper bounds as well as lower bounds for the general case. Furthermore, we also consider the LeadingOnes fitness function.},
added-at = {2017-09-19T19:30:48.000+0200},
author = {Friedrich, Tobias and Kötzing, Timo and Lagodzinski, J. A. Gregor and Neumann, Frank and Schirneck, Martin},
biburl = {https://www.bibsonomy.org/bibtex/2d98514defd982bb6fa08ecf2e4583f41/typo3tester},
booktitle = {Foundations of Genetic Algorithms (FOGA)},
interhash = {76dbd82e0afc2b00eea3ca5c5a6b41c4},
intrahash = {d98514defd982bb6fa08ecf2e4583f41},
keywords = {testing typo3},
pages = {45-54},
publisher = {ACM Press},
timestamp = {2017-09-19T19:30:48.000+0200},
title = {Analysis of the {(1+1)} EA on Subclasses of Linear Functions under Uniform and Linear Constraints},
year = 2017
}