Profiling communication in distributed genetic algorithms

Authors:
Jonathan Maresky;Yuval Davidor;Daniel Gitler;Gad Aharoni;Amnon Barak
Affiliations:
Institute of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;Schema-Evolutionary Algorithms Ltd., Herzlia and Institute of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;Institute of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;Institute of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;Institute of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel
Venue:
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Year:
1995

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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.