---
title: "Profiling Communication in Distributed Genetic Algorithms"
authors:
  - "Jonathan Maresky"
  - "Yuval Davidor"
  - "Daniel Gitler"
  - "Gad Aharoni"
  - "Amnon Barak"
doi: ""
url: "https://ijcai.org/proceedings/1995/124"
pdf: "https://ijcai.org/proceedings/1995/0124.pdf"
venue: "Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence"
year: 1995
pages: "961-965"
track: "Invited Speakers"
keywords:
---

# Profiling Communication in Distributed Genetic Algorithms

Jonathan Maresky, Yuval Davidor, Daniel Gitler, Gad Aharoni, Amnon Barak

*Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence*, 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.