Dana Vrajitoru
ABSTRACT
In this paper we present a concurrent implementation of genetic algorithms designed for shared memory architectures, intended to take advantage of multi-core processor platforms. Our algorithm divides the problems into sub-problems along the chromosome as opposed to the usual approach of dividing the population into niches. We show tests for timing and performance on a variety of platforms
- E. Alba and M. Tomassini. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 6(5):443--462, 2002. Google Scholar
Digital Library
- B. Baker, C. Carter, and G. Dozier. Sema: A new paradigm for distributed genetic and evolutionary computating. In B. Kim, editor, Proceeding of the Midwest Artificial Intelligence and Cognitive Science Conference, pages 35--39, Fort Wayne, IN, 2009.Google Scholar
- E. Cantú-Paz. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2):141--171, 1998. Paris: Hermes.Google Scholar
- E. Cantú-Paz. Migration polices, selection pressure, and parallel evolutionary algorithms. Journal of heuristics, 7(4):311--334, 2001. Google ScholarDigital Library
- G. Dozier. A comparison of static and adaptive replacement strategies for distributed steady-state evolutionary path planning in non-stationary environments. International Journal of Knowledge-Based Intellident Engineering Systems (KES), 7(1):1-8, January 2003.Google Scholar
- N. Getz. Control of balance for a nonlinear nonholonomic no-minimum phase model of a bicycle. In American Control Conference, Baltimore, June 1994.Google ScholarCross Ref
- K. B. Harvey and C. C. Pettey. The outlaw method for solving multimodal functions with split ring parallel genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 274--280, Orlando (FL), 1999. Morgan Kaufmann Publishers.Google Scholar
- M. Kim, V. Aggarwal, U. O'Reilly, and M. Médard. A doubly distributed genetic algorithm for network coding. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1272--1279, London, UK, July 7-11 2007. ACM. Google ScholarDigital Library
- X. Llorà, R. Reddy, B. Matesic, and R. Bhargava. Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 2098--2105, London, UK, July 7-11 2007. ACM. Google ScholarDigital Library
- K. Sastry, D. Goldberg, and X. Llorà. Towards billion-bit optimization via a parallel estimation of distribution algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 577--584, London, UK, July 7-11 2007. ACM. Google ScholarDigital Library
- I. Sekaj and M. Oravec. Selected population characteristics of fine-grained parallel genetic algorithms with re-initialization. In GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pages 945--948, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- N. Getz. Control of balance for a nonlinear nonholonomic no-minimum phase model of a bicycle. In American Control Conference, Baltimore, June 1994.Google ScholarCross Ref
- K. B. Harvey and C. C. Pettey. The outlaw method for solving multimodal functions with split ring parallel genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 274--280, Orlando (FL), 1999. Morgan Kaufmann Publishers.Google Scholar
- M. Kim, V. Aggarwal, U. O'Reilly, and M. Médard. A doubly distributed genetic algorithm for network coding. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1272--1279, London, UK, July 7-11 2007. ACM. Google ScholarDigital Library
- X. Llorà, R. Reddy, B. Matesic, and R. Bhargava. Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 2098--2105, London, UK, July 7-11 2007. ACM. Google ScholarDigital Library
- K. Sastry, D. Goldberg, and X. Llorà. Towards billion-bit optimization via a parallel estimation of distribution algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 577--584, London, UK, July 7-11 2007. ACM. Google ScholarDigital Library
- I. Sekaj and M. Oravec. Selected population characteristics of fine-grained parallel genetic algorithms with re-initialization. In GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pages 945--948, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- D. Vrajitoru. Soft Computing in Information Retrieval. Techniques and Applications, chapter Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval, pages 199--222. Physica-Verlag, Heidelberg, Germany, 2000.Google Scholar
- D. Vrajitoru. Parallel genetic algorithms based on coevolution. In R. K. Belew and H. Juillé, editors, Proceedings of the Genetic and Evolutionary Computation Conference, Late breaking papers, pages 450--457, 2001.Google Scholar
- D. Vrajitoru and R. Mehler. Multi-agent autonomous pilot for single-track vehicles. In Proceedings of the IASTED Conference on Modeling and Simulation, Oranjestad, Aruba, 2005.Google Scholar
- K. De Jong Z. Skolicki. The influence of migration sizes and intervals on island models. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1295--1302, Washington DC, USA, June 25-29 2005. Google ScholarDigital Library
Index Terms
- Shared memory genetic algorithms in a multi-agent context
Recommendations
Parallel Single and Multiple Objectives Genetic Algorithms: A Survey
This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an ...
Scalable Parallel Genetic Algorithms
Genetic algorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, genetic algorithms exhibit high ...
Parallelisation of genetic algorithms for the 2-page crossing number problem
Genetic algorithms (GAs) have been applied to solve the 2-page crossing number problem successfully, but since they work with one global population, the search time and space are limited. Parallelisation provides an attractive prospect to improve the ...
Comments