Profiling Communication in Distributed Genetic Algorithms
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Invited Speakers. Pages 961-965.
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.
@inproceedings{ijcai1995p124,
title = {Profiling Communication in Distributed Genetic Algorithms},
author = {Maresky, Jonathan and Davidor, Yuval and Gitler, Daniel and Aharoni, Gad and Barak, Amnon},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {961--965},
year = {1995},
note = {Invited Speakers},
url = {https://ijcai.org/proceedings/1995/124},
}
TY - CPAPER TI - Profiling Communication in Distributed Genetic Algorithms AU - Maresky, Jonathan AU - Davidor, Yuval AU - Gitler, Daniel AU - Aharoni, Gad AU - Barak, Amnon T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 961 EP - 965 UR - https://ijcai.org/proceedings/1995/124 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: