Abstract
This paper investigates the sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The distribution of these samples can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW), Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems.
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Leung, K.S., Lee, K.H., Cheang, S.M.: Evolving Parallel Machine Programs for a Multi-ALU Processor. Proc. of IEEE Congress on Evolutionary Computation (2002) 1703–1708
Leung, K.S., Lee, K.H., Cheang, S.M.: Balancing Samples’ Contributions on GA Learning. Proc. of the 4th Int. Conf. on Evolvable Systems: From Biology to Hardware (ICES), Lecture Notes in Computer Science, Springs-Verlag (2001) 256–266
Leung, K.S., Lee, K.H., Cheang, S.M.: Parallel Programs are More Evolvable than Sequential Programs. Proc. of the 6th Euro. Conf. on Genetic Programming (EuroGP), Lecture Notes in Computer Science, Springs-Verlag (2003)
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Cheang, S.M., Lee, K.H., Leung, K.S. (2003). Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_72
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DOI: https://doi.org/10.1007/3-540-45110-2_72
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