A Jxta Based Asynchronous Peer-to-Peer Implementation of Genetic Programming

Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peerto-Peer (P2P) computing has recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we present P-CAGE: a P2P environment for Genetic Programming based on the JXTA protocols. P-CAGE is based on a hybrid multi-island model that combines the island model with the cellular model. Each island adopts a cellular model and the migration occurs between neighboring peers placed in a virtual ring topology. Three different termination criteria (effort, time and maxgen) have been implemented. Experiments were conducted on some popular benchmarks and scalability, accuracy and the effect of migration have been studied. Performance are at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralization, fault tolerance and scalability of P2P systems. We also demonstrated the important effect of migration in accelerating the convergence.

[1]  Bastien Chopard,et al.  Parallel Genetic Programming and its Application to Trading Model Induction , 1997, Parallel Comput..

[2]  Kay Chen Tan,et al.  A P2P genetic algorithm environment for the internet , 2005, CACM.

[3]  John R. Koza,et al.  Parallel Genetic Programming on a Network of Transputers , 1995 .

[4]  Prabhas Chongstitvatana,et al.  Comparison Between Synchronous and Asynchronous Implementation of Parallel Genetic Programming , 1999 .

[5]  Lee Spector,et al.  High-performance, parallel, stack-based genetic programming , 1996 .

[6]  Erick Cantú-Paz,et al.  A Summary of Research on Parallel Genetic Algorithms , 1995 .

[7]  Marco Tomassini,et al.  Experimental study of isolated multipopulation genetic programming , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[8]  Dimitris C. Dracopoulos,et al.  Speeding up genetic programming: a parallel BSP implementation , 1996 .

[9]  Leonardo Vanneschi,et al.  Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming , 2001, EuroGP.

[10]  Hugues Juill.  Parallel Genetic Programming on Fine-Grained SIMD Architectures , 2001 .

[11]  Jordan B. Pollack,et al.  Massively parallel genetic programming , 1996 .

[12]  Hugh Glaser,et al.  Parallel Implementation of a Genetic-Programming Based Tool for Symbolic Regression , 1998, Inf. Process. Lett..

[13]  John R. Koza,et al.  A Parallel Implementation of Genetic Programming that Achieves Super-Linear Performance , 1998, Inf. Sci..

[14]  Leonardo Vanneschi,et al.  Experimental Investigation of Three Distributed Genetic Programming Models , 2002, PPSN.

[15]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[16]  Dalius Rubliauskas,et al.  Flocking of subpopulations in distributed genetic programming , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[17]  Kenji Satou,et al.  ASYNCHRONOUS PEER-TO-PEER COMMUNICATION FOR FAILURE RESILIENT DISTRIBUTED GENETIC ALGORITHMS , 2003 .

[18]  Hitoshi Iba,et al.  Distributed genetic programming: empirical study and analysis , 1996 .

[19]  Marco Tomassini,et al.  A Parallel Genetic Programming Tool Based on PVM , 1999, PVM/MPI.

[20]  Leonardo Vanneschi,et al.  A Distributed Computing Environment for Genetic Programming Using MPI , 2000, PVM/MPI.

[21]  Leonardo Vanneschi,et al.  An Empirical Study of Multipopulation Genetic Programming , 2003, Genetic Programming and Evolvable Machines.

[22]  Dimitris C. Dracopoulos,et al.  Bulk Synchronous Parallelisation of Genetic Programming , 1996, PARA.

[23]  James P. Cohoon,et al.  C6.3 Island (migration) models: evolutionary algorithms based on punctuated equilibria , 1997 .

[24]  Giandomenico Spezzano,et al.  A scalable cellular implementation of parallel genetic programming , 2003, IEEE Trans. Evol. Comput..

[25]  Leslie G. Valiant,et al.  A bridging model for parallel computation , 1990, CACM.

[26]  C. Pettey.  Diffusion (cellular) models , 2000 .

[27]  Marco Tomassini,et al.  Parallel genetic programming: an application to trading models evolution , 1996 .

[28]  Francisco Fernández,et al.  Comparing Synchronous and Asynchronous Parallel and Distributed Genetic Programming Models , 2002 .

[29]  Ankur Teredesai,et al.  Genetic Programming in Wireless Sensor Networks , 2005, EuroGP.

[30]  L. Darrell Whitley,et al.  Cellular Genetic Algorithms , 1993, ICGA.

[31]  William Yurcik,et al.  VRing: a case for building application-layer multicast rings (rather than trees) , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[32]  William F. Punch.  HOW EFFECTIVE ARE MULTIPLE POPULATIONS IN GENETIC PROGRAMMING , 1998 .