Name
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. |
Year | Venue | Keywords |
|---|---|---|
1995 | IJCAI (1) | genetic information,improvement history,global average improvement curve,profiling communication,parallel execution,additional information,genetic algorithm,traditional communication approach,own performance,genetic algorithm execution,execution performance,genetics |
Field | DocType | ISSN |
Profiling (computer programming),Computer science,Theoretical computer science,Artificial intelligence,Genetic representation,Quality control and genetic algorithms,Genetic algorithm,Machine learning | Conference | 1045-0823 |
ISBN | Citations | PageRank |
1-55860-363-8 | 1 | 0.46 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Maresky | 1 | 15 | 2.10 |
| Yuval Davidor | 2 | 266 | 106.23 |
| Daniel Gitler | 3 | 15 | 2.10 |
| Gad Aharoni | 4 | 24 | 3.70 |
| Amnon Barak | 5 | 590 | 119.00 |