A Robust Master-Slave Distribution Architecture for Evolutionary Computations

This paper presents a new robust masterslave distribution architecture for multiple populations Evolutionary Computations (EC). It discusses the main advantages and drawbacks of master-slave models over island models for parallel and distributed EC. It also formulates a mathematical model of the master-slave distribution policies in order to show that, contrary to what is implied by current mainstream developments in island models, a well designed master-slave approach can be both robust and scalable (up to a certain point). Finally, it introduces some of the details of a new C++ framework named Distributed BEAGLE, which implements this architecture over the Open BEAGLE EC framework.

[1]  John R. Koza,et al.  Parallel genetic programming: a scalable implementation using the transputer network architecture , 1996 .

[2]  Alessandro Bollini,et al.  Distributed and Persistent Evolutionary Algorithms: A Design Pattern , 1999, EuroGP.

[3]  T.C. Fogarty,et al.  A Distributed Resource Evolutionary Algorithm Machine (DREAM) , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[5]  Ben Paechter,et al.  A Framework for Distributed Evolutionary Algorithms , 2002, PPSN.

[6]  Ben Paechter,et al.  A scalable and robust framework for distributed applications , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  Marc Parizeau,et al.  Open BEAGLE: A New Versatile C++ Framework for Evolutionary Computation , 2002, GECCO Late Breaking Papers.