Asynchronous Evolution by Reference-Based Evaluation: Tertiary Parent Selection and Its Archive
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This paper proposes a novel asynchronous reference-based evaluation (named as ARE) for an asynchronous EA that evolves individuals independently unlike general EAs that evolve all individuals at the same time. ARE is designed for an asynchronous evolution by tertiary parent selection and its archive. In particular, ARE asynchronously evolves individuals through a comparison with only three of individuals (i.e., two parents and one reference individual as the tertiary parent). In addition, ARE builds an archive of good reference individuals. This differ from synchronous evolution in EAs in which selection involves comparison with all population members. In this paper, we investigate the effectiveness of ARE, by applying it to some standard problems used in Linear GP that aim being to minimize the execution step of machine-code programs. We compare GP using ARE (ARE-GP) with steady state (synchronous) GP (SSGP) and our previous asynchronous GP (Tierra-based Asynchronous GP: TAGP). The experimental results have revealed that ARE-GP not only asynchronously evolves the machine-code programs, but also outperforms SSGP and TAGP in all test problems.
KeywordsGenetic programming asynchronous evolution machine-code program
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- 1.Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)Google Scholar
- 2.Koza, J.: Genetic Programming On the Programming of Computers by Means of Natural Selection. MIT Press (1992)Google Scholar
- 5.Harada, T., Takadama, K.: Asynchronous evaluation based genetic programming: Comparison of asynchronous and synchronous evaluation and its analysis. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 241–252. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 6.Ray, T.S.: An approach to the synthesis of life. Artificial Life II XI, 371–408 (1991)Google Scholar
- 7.Reynolds, C.W.: An evolved, vision-based behavioral model of coordinated group motion. In: Proc. 2nd International Conf. on Simulation of Adaptive Behavior, pp. 384–392. MIT Press (1993)Google Scholar
- 8.Banzhaf, W., Francone, F.D., Keller, R.E., Nordin, P.: Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
- 9.Brameier, M.F., Banzhaf, W.: Linear Genetic Programming, vol. 117. Springer (2007)Google Scholar
- 10.Microchip Technology Inc.: PIC10F200/202/204/206 Data Sheet 6-Pin, 8-bit Flash Microcontrollers. Microchip Technology Inc. (2007)Google Scholar