Abstract
Genetic programming (GP) has been used successfully as a technique for constructing robot control programs. Depending on the number of evaluations and the cost of each evaluation however, GP may require a substantial amount of processing time to find a feasible solution. The advent of parallel GP has brought the execution time of GP to a more acceptable level. This paper investigates parallel GP with a mobile robot navigation problem. The parallel implementations are based on a coarse-grained model. A technique for distributing the task of serial GP is proposed. In particular, this technique shows that the total amount of work can be reduced while maintaining the quality of the solutions. Asynchronous and synchronous implementations are examined. We compare the performance in terms of both the solution quality and the execution time. The timing analysis is investigated to give an insight into the behavior of parallel implementations. The results show that the parallel algorithm with asynchronous migration using 10 processors is 33 times faster than the serial algorithm.
Similar content being viewed by others
References
Chongstitvatana P (1998) Improving robustness of robot programs generated by genetic programming for dynamic environments. Proceedings of IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Chiang Mai, Thailand, November 24–27, 1998, p. 523–526
Chongstitvatana P (1999) Using perturbation to improve robustness of solutions generated by genetic programming for robot learning. J Cicruits Syst Comput 9:133–143.
Becker DJ, Sterling T, Savarese D, et al. (1995) BEOWULF: a parallel workstation for scientific computation. Proceedings of the 1995 International Conference on Parallel Processing, vol. 1, p 11–14.
Tongchim S, Chongstitvatana P (1999) Speed-up improvement on automatic robot programming by parallel genetic programming. Proceedings of the 1999 IEEE International Sympsoium on Intelligent Signal Processing and Communication Systems (ISPACS), Phuket, Thailand, p 77–80.
Cantú-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs Parallèles Reseaux Systems Repartis 10:141–171.
Koza JR, Andre D (1995) Parallel genetic programming on a network of transputers. In: Rosca J (ed) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, University of Rochester, National Resource Laboratory for the Study of Brain and Behavior, p 111–120
Dracopoulos DC, Kent S (1996) Bulk synchronous parallelisation of genetic programming. Proceedings of the 3rd International Workshop on Applied Parallel Computing in Industrial Problems and Optimization (PARA), Lyngby, Denmark, August 18–21, 1996, p 216–226
Niwa T, Iba H (1996) Distributed genetic programming: empirical study and analysis. Proceedings of the 1st Annual Conference on Genetic Programming, Stanford University, California, July 28–31, 1996, MIT Press, Cambridge, p 339–344
Punch B (1998) How effective are multiple populations in genetic programming? Proceedings of the 3rd Annual Conference on Genetic Programming, Madison, WI, July 22–25, 1998, Morgan Kaufmann, San Francisco, p 308–313
Chong FS (1998) A Java-based distributed approach to genetic programming on the Internet. Master thesis, University of Birmingham
Cantú-Paz E (1999) Designing efficient and accurate parallel genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign
Gustafson J (1990) Fixed time, tiered memory, and superlinear speed-up. Proceedings of the 5th Distributed Memory Computing Conference (DMCC 5), Charleston
Andre D, Koza JR (1996) A parallel implementation of genetic programming that achieves super-linear performance. In: Arabnia HR (ed) Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, Athens, GA, CSREA, Sunnyvale, vol 3, p 1163–1174.
Lin S-C, Punch WF, Goodman ED (1994) Coarse-grain parallel genetic algorithms: categorization and new approach. Proceedings of the 6th IEEE Symposium on Parallel and Distributed Processing, October 26–29, 1994, p 28–37.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Tongchim, S., Chongstitvatana, P. Parallel genetic programming: Synchronous and asynchronous migration. Artif Life Robotics 5, 189–194 (2001). https://doi.org/10.1007/BF02481500
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02481500