One of the strongest advantages of Distributed Evolutionary Algorithms (DEAs) is that they can be implemented in distributed environment of heterogeneous computing nodes. Usually such computing nodes differ in hardware and operating systems. Distributed systems are limited by network latency. Some Evolutionary Algorithms (EAs) are quite suitable for distributed computing implementation, because of their high level of parallelism and relatively less intensive network communication demands. One of the most widely used topologies for distributed computing is the star topology. In a star topology there is a central node with global EA population and many remote computation nodes which are working on a local population (usually sub-population of the global population). This model of distributed computing is also known as island model. What is common for DEAs is an operation called migration that transfers some individuals between local populations. In this paper, the term 'distribution' will be used instead of the term 'migration', because it is more accurate for the model proposed. This research proposes a strategy for distribution of EAs individuals in star topology based on incident node participation (INP). Solving the Rubik's cube by a Genetic Algorithm (GA) will be used as a benchmark. It is a combinatorial problem and experiments are done with a C++ program which uses OpenMPI.
1. Goldberg, D. E. Genetic and Evolutionary Algorithms Come of Age. – Communications of the ACM, Vol. 37, 1994, No 3, pp. 113-119.
2. Pappa, G., G. Ochoa, M. Hyde, A. Freitas, J. Woodward, J. Swan. Contrasting Meta-Learning and Hyper-Heuristic Research: The Role of Evolutionary Algorithms. – Genetic Programming and Evolvable Machines, Vol. 15, 2014, Issue 1, pp. 3-35.
3. Adamidis, P. Review of Parallel Genetic Algorithms Bibliography. Tech. Rep. Version 1. Aristotle University of Thessaloniki, Thessaloniki, Greece, 1994.
4. Gordon, V. S., D. Whitley. Serial and Parallel Genetic Algorithms as Function Optimizers. – In: S. Forrest, Ed. Proc. of 5th International Conference on Genetic Algorithms, Morgan Kaufmann (San Mateo, CA), 1993, pp. 177-183.
5. Lin, S.-C., W. Punch, E. Goodman. Coarse-Grain Parallel Genetic Algorithms – Categorization and New Approach. – In: Proc. of 6th IEEE Symposium on Parallel and Distributed Processing, IEEE Computer Society Press, 1994, Los Alamitos, CA.
6. Tanese, R. Distributed Genetic Algorithms. – In: J. D. Schaer, Ed., Proc. of 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, 1989, pp. 434-439.
7. Uchida, T., T. Matsuzawa, Y. Inoguchi. The Inuence of Elitism Strategy on Migration Intervals of a Distributed Genetic Algorithm. – Proceedings in Adaptation, Learning and Optimization, Vol. 2, 2015, pp. 363-374.
9. Ivanov, V. Using a PicoBlaze Processor to Traffic Light Control. – Cybernetics and Information Technologies, 2015, pp. 131-139.
10. Tashev, T., V. Monov. Modeling of the Hotspot Load Traffic for Crossbar Switch Node by Means of Generalized Nets. – In: Proc. of 6th IEEE International Conference Intelligent Systems, 2012, Sofia, Bulgaria, pp.187-191.
11. Penev, K. Free Search in Multidimensional Space II. – Numerical Methods and Applications Lecture Notes in Computer Science, Vol. 8962, 2015, pp. 103-111.
12. Korf, R. Finding Optimal Solutions to Rubik’s Cube Using Pattern Databases. – In: Proc. AAAI-98, Madison, WI, AAAI Press, Menlo Park, CA, 1998, pp. 700-705.
13. Randall, K. Cilk: Efficient Multithreaded Computing. Doctor of Philosophy in Computer Science and Engineering, Massachusetts Institute of Technology, 1998.
14. Antonisse, H. A Grammar-Based Genetic Algorithm. Foundations of Genetic Algorithms, Indiana University, Bloomington, USA, 1991, pp. 193-204.
15. Poli, R., J. Koza. Genetic Programming. – Search Methodologies, 2014, pp. 143-185.
16. Whitley, D., T. Starkweather, C. Bogart. Genetic Algorithms and Neural Networks: Optimizing Connections and Connectivity. – Parallel Computing, Vol. 3, 1990, Issue 3, pp. 347-361.
17. Guliashki, V., L. Kirilov. Hybrid Evolutionary Algorithm for Multiple Objective Convex Integer Problems. – In: Proc. of 28th International Conference on Information Technologies (InfoTech-2014), Varna, St. St. Constantine and Elena Resort, Bulgaria, 2014, pp. 19-28.
18. Gabriel, E., G. Fagg, G. Bosilca, T. Angskun, J. Dongarra, J. Squyres, V. Sahay, P. Kambadur, B. Barrett, A. Lumsdaine, R. Castain, D. Daniel, R. Graham, T. Woodall. Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation, Recent Advances in Parallel Virtual Machine and Message Passing Interface. – In: Lecture Notes in Computer Science, Vol. 3241, 2004, pp. 97-104.