Profiling Communication in Distributed Genetic Algorithms
Abstract
To what extent is distribution beneficial to the search quality and computational resources used by a genetic algorithm execution? Most distributed genetic algorithms rely on communicating genetic information, in the form of individual solutions, between concurrently evolving populations. Another way of effectively using the additional information generated by the parallel executions is the profiling approach to communication, where populations decide whether their own performance is satisfactory, relative to the global average improvement curve. Thus, communication between populations takes the form of improvement histories. This is shown to improve on the traditional communication approach, in terms of both solution quality and execution performance.
Cite
Text
Maresky et al. "Profiling Communication in Distributed Genetic Algorithms." International Joint Conference on Artificial Intelligence, 1995.Markdown
[Maresky et al. "Profiling Communication in Distributed Genetic Algorithms." International Joint Conference on Artificial Intelligence, 1995.](https://mlanthology.org/ijcai/1995/maresky1995ijcai-profiling/)BibTeX
@inproceedings{maresky1995ijcai-profiling,
title = {{Profiling Communication in Distributed Genetic Algorithms}},
author = {Maresky, Jonathan and Davidor, Yuval and Gitler, Daniel and Aharoni, Gad and Barak, Amnon},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1995},
pages = {961-966},
url = {https://mlanthology.org/ijcai/1995/maresky1995ijcai-profiling/}
}