Paper Info
Title
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling.
Abstract
Evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very effective in dynamically tracking changes made to the problem instance.
Year
Venue
Field
2015
IJCAI
Memetic algorithm,Mathematical optimization,Job shop scheduling,Evolutionary algorithm,Scheduling (computing),Computer science,Local search (optimization),Evolutionary programming,Optimization problem,Computational complexity theory
DocType
Volume
Citations 
Journal
abs/1504.06363
2
PageRank 
References 
Authors
0.37
21
2
Name
Order
Citations
PageRank
Frank Neumann11727124.28
Carsten Witt298759.83