Parallel Genetic Algorithms



That ye might walk worthy of the Lord unto all pleasing, being fruitful in every good work, and increasing in the knowledge of God. Colossians 1:10

In this research, we are exploring the benefits and drawbacks of several distributed system architectures in developing an implementation of a distributed GA that exploits the Jini and JavaSpace technologies. Our results, using the knapsack problem as an illustration, show that there is an unavoidable price to pay in terms of decreasing computation to communication ratios as a function of instance size. However, we can diminish these effects by expanding the number of JavaSpaces beyond those required for the obvious implementation. Our results also indicate that as the number of remote machines increases the potential for a better solution also rises. Even though our distributed GAs did not always exploit this potential for a higher quality solution, we believe that the combination of Java, Jini, and JavaSpaces presents avenues for easily distributing the computation of genetic algorithms. Currently, we are investigating the impact that topologies, migration rates, and migrant selection heuristics have on the convergence time when the solution quality is fixed. Furthermore, we are experimenting with the creation of parallel genetic algorithms that use the Java Messaging Service (JMS).

Related Papers:

Related Presentations:

Project Team:

  • Gregory M. Kapfhammer (Allegheny College)
  • Robert S. Roos (Allegheny College)
  • Brian Zorman (Allegheny College)
  • Christopher McNamara (Allegheny College)

This research project is supported by research and travel grants from the Department of Computer Science and the Dean's Office at Allegheny College. Please refer to Research for a complete listing of my current research activities.


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