Abstract
Designed an evaluation function with parameters, and used genetic algorithm to optimize the parameters. This paper considers the objective function’s variation trends in searching point and the information is added to the fitness function to guide the searching. Simultaneously adaptive genetic algorithm enables crossover probability and mutation probability automatically resized according to the individual’s fitness. These measures have greatly improved the convergence rate of the algorithm. Sparring algorithm is introduced to guide the training, using gradient training programs to save training time. Experiments show skills in playing Dots-and-Boxes are greatly improved after its evaluation function parameters are optimized.
F. Bi—Fund Project: National Natural Science Foundation and Shanxi Provincial People’s Government Jointly Funded Project of China for Coal Base and Low Carbon (No. U1510115), the Qing Lan Project, the China Postdoctoral Science Foundation (No. 2013T60574).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hongkun, Q., Peng, Z., Yajie, W., et al.: Analysis of search algorithm in computer game of Amazons. In: 26th Chinese Control and Decision Conference, pp. 3947–3950. IEEE Press, New York (2014)
Duan, Z.: An improved evaluation function for Connect6. In: 24th Chinese Control and Decision Conference, pp. 1685–1690. IEEE Press, New York (2012)
Wei, X.-K., Shao, W., Zhang, C., et al.: Improved self-adaptive genetic algorithm with quantum scheme for electromagnetic optimization. IET Microw. Antennas Propag. 8, 965–972 (2014)
He, X., Liang, J.: The objective function using genetic algorithms gradient. J. Softw. 12, 981–985 (2001). (in Chinese)
Luo, B., Zheng, J., Yang, P.: GA-based directional climbing. Comput. Eng. Appl. 44, 92–95 (2008). (in Chinese)
Qi, J.-Y.: Application of improved simulated annealing algorithm in facility layout design. In: 29th Chinese Control Conference, pp. 5224–5227. IEEE Press, New York (2010)
Li, S., Li, D., Yuan, X.: Research and implementation of dots-and-boxes. J. Softw. 7, 256–262 (2012)
Deng, X.: Application of adaptive genetic algorithm in inversion analysis of permeability coefficients. In: Second International Conference on Genetic and Evolutionary Computing, WGEC 2008, pp. 61–65. IEEE Press, New York (2014)
Huang, Y.-P., Chang, Y.-T., Sandnes, F.-E.: Using fuzzy adaptive genetic algorithm for function optimization. In: Annual Meeting of the North American on Fuzzy Information Processing Society, NAFIPS 2006, pp. 484–489. IEEE Press, New York (2006)
Yanhong, P.: Wind power fitness function calculation based on niche genetic algorithm. In: International Conference on Sustainable Power Generation and Supply, pp. 1–5. IEEE Press, New York (2012)
Stockman, G.C.: A minimax algorithm better than alpha-beta? Artif. Intell. 12, 179–196 (1978)
Ibaraki, T.: Generalization of alpha-beta and SSS* search procedures. Artif. Intell. 29, 73–117 (1986)
Plaat, A., Schaeffer, J., Pijls, W., de Bruin, A.: SSS* = alphabet + TT. Technical report TR-CS_94-17, Department of Computing Science, University of Alberta, Edmonton, AB, Canada (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Bi, F., Wang, Y., Chen, W. (2017). Adaptive Genetic Algorithm to Optimize the Parameters of Evaluation Function of Dots-and-Boxes. In: Lee, JH., Pack, S. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-319-60717-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-60717-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60716-0
Online ISBN: 978-3-319-60717-7
eBook Packages: Computer ScienceComputer Science (R0)