On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling
Data Structures and Algorithms
2015-04-27 v1 Neural and Evolutionary Computing
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.
Cite
@article{arxiv.1504.06363,
title = {On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling},
author = {Frank Neumann and Carsten Witt},
journal= {arXiv preprint arXiv:1504.06363},
year = {2015}
}
Comments
Conference version appears at IJCAI 2015