ABSTRACT
Evolutionary Electronics (EE) is a research area which involves application of Evolutionary Computation in the domain of electronics. EE algorithms are generally able to find good solutions to rather small problems in a reasonable amount of time, but the need for solving more and more complex problems increases the time required to find adequate solutions. This is due to the large number of individuals to be evaluated and to the large number of generations required until the convergence process leads to the solution. As a consequence, there have been multiple efforts to make EE faster, and one of the most promising choices is to use distributed implementations. In this paper, we propose a cluster-based evolutionary design of digital circuits using a distributed improved multi expression programming method (DIMEP). DIMEP keeps, in parallel, several sub-populations that are processed by Impoved Multi-Expression Programming algorithms, with each one being independent from the others. A migration mechanism produces a chromosome exchange between the subpopulations using MPI (Message Passing Interface) on a dedicated cluster of workstations (Lido Cluster, Dortmund University). This paper presents the main ideas and shows preliminary experimental results.
- Alba, E. and Tomassini, M., Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation, 6, 5, (2002), 443--462. Google ScholarDigital Library
- Banzhaf, W., Nordin, P., Keller, R. E. and Francone, F. D., Generic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, 1998. Google ScholarDigital Library
- Bellman, R., Dynamic Programming, Princeton University Press, New Jersey, 1957. Google ScholarDigital Library
- Cantú-Paz, E., A survey of parallel genetic algorithms, Calculateurs Paralleles, Reseaux et Systems Repartis, 10, 2, (1998), 141--171.Google Scholar
- Cheang, S. M., Leung, K. S., and Lee, K. H., Genetic Parallel Programming: Design and Implementation, Evolutionary Computation, 14, 2, (2006), 129--156. Google ScholarDigital Library
- Coello, C. A. C., Alba, E. and Luque, G., Comparing Different Serial and Parallel Heuristics to Design Combinational Logic Circuits. In Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware, 2003, 3--12. Google ScholarDigital Library
- Corcoran, A. L. and Wainwright, R. L., A parallel island model genetic algorithm for the multiprocessor scheduling problem. In Proceedings of the 1994 ACM/SIGAPP Symposium on Applied Computing, 1994, 483--487. Google ScholarDigital Library
- Fernandez, F. and Tomassini, M., Improving Parallel Genetic Algorithm GA Performance by Means of Plagues, Advances in Soft Computing 2, Springer Verlag, Berlin, Heidelberg, (2005), 515--523.Google Scholar
- Fernández, F., Tomassini, M. and Vanneschi, L., Studying the influence of Communication Topology and Migration on Distributed Genetic Programming, In J. Miller, M. Tomassini, P. L. Lanzi, C. Ryan, A. G. B. Tettamanzi, W. Landdon, LNCS 2038 Genetic Programming, 4th European Conference, EuroGP 2001, Springer Verlag, Berlin, (2001), 51--63. Google ScholarDigital Library
- Ferreira, C., Gene Expression Programing: A New Adaptive Algorithm for Solving Problems, Complex Systems, 13, (2001), 87--129.Google Scholar
- Hadjam F. Z., Moraga C., and Hildebrand L.: Evolutionary design of digital circuits using Improved Multi-Expression Programming. Research Report 812, Faculty of Informatics, University of Dortmund, Germany, 2007. (Copy may be obtained from the authors).Google Scholar
- Herrera F, Lozano M, Moraga C.: Hybrid distributed real-coded genetic algorithms. Lecture Notes in Computer Science 1498, Springer Verlag, Berlin (1998), 603--612. Google ScholarDigital Library
- Koza, J. R. and Andre, D., Parallel genetic programming on a network of transputers, Proc. 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. Technical Report 95-2, 111--120, 1995. Google ScholarDigital Library
- Koza, J. R., Genetic Programming. On the Programming of Computers by means of Natural Selection, MIT Press, 1992. Google ScholarDigital Library
- Miller, J. F., and Thomson, P., Cartesian Genetic Programming. In Proc. of the 3rd International Conference on Genetic Programming (EuroGP2000), LNCS 1082, Springer-Verlag, Berlin, (2000), 15--17. Google ScholarDigital Library
- Miller, J. F., Job. D. and Vassilev, V. K., Principles in the Evolutionary Design of digitals circuits - Part I, Genetic Programming and Evolvable Machines, 1, 1, (2000), 7--35. Google ScholarDigital Library
- Oltean M., Solving Even-Parity Problems using Multi Expression Programming, Proceedings of the 5th International Workshop on Frontiers in Evolutionary Algorithms, The 7th Joint Conference on Information Sciences, (September 26-30, 2003, Research Triangle Park, North Carolina), Edited by Ken Chen (et. al), 2003, 315--318.Google Scholar
- Oltean M., Multi-expression Programming, Technical Report, Babes-Bolyai Univ, Romania, 2006.Google Scholar
- Oltean, M. and Grosan, C., Solving Classification Problems using Infix Form Genetic Programming, The Fifth International Symposium on Intelligent Data Analysis, edited by M. Berthold (et. al), LNCS 2810, Springer Verlag, Berlin, (2003), 242--252.Google ScholarCross Ref
- Oltean, M. and Grosan, C., A Comparison of Several Linear Genetic Programming Techniques, Complex-Systems, 14, 4, (2003), 282--311.Google Scholar
- Oltean, M. and Grosan, C., Evolving Digital Circuits using Multi Expression Programming. NASA/DoD Conference on Evolvable Hardware, (24-26 June, Seattle), Edited by R. Zebulum (et. al), IEEE Press, NJ, 2004, 87--90.Google ScholarCross Ref
- Patterson, N., Genetic Programming with Context-Sensitive Grammars, Ph.D. Thesis, School of Computer Science, University of Scotland, 2002.Google Scholar
- Ryan, C., O'Neill, M., Grammatical Evolution: A Steady State Approach, Late Breaking Paper, Genetic Programing Edited by J.R. Koza (University of Wisconsin, Madison, Wisconsin, 1998).Google Scholar
- Skolicki, Z., An Analysis of Island Models in Evolutionary Computation. In Proceeding of the 2005 workshops on Genetic and evolutionary computation, (Washington, D.C. June 2005), 2005, 25--26. Google ScholarDigital Library
Index Terms
Cluster-based evolutionary design of digital circuits using all improved multi-expression programming
Recommendations
Population variation in genetic programming
A new population variation approach is proposed, whereby the size of the population is systematically varied during the execution of the genetic programming process with the aim of reducing the computational effort compared with standard genetic ...
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving ...
Fitness tracking based evolutionary programming: a novel approach for function optimization
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationIn order to achieve a satisfactory optimization performance by evolutionary programming (EP), it is necessary to ensure proper balance between exploration and exploitation. It is obvious that one single mutation operator is not the answer. Moreover, ...
Comments