Abstract Automatic Chinese Tang poetry composition arouses researchers' attention these years and faces a lot of challenges. Most existing poetry generation systems can only generate poems without human interaction; thus, these poems cannot always express the human mind accurately. To improve this disadvantage, this paper proposes a modified elitist genetic algorithm to generate poetry with arbitrary interaction from the user, which means that the user can specify the poem’s emotion and input words or verses to be used in the poem. The modified algorithm comprises an improved elitist strategy to retain keywords or verses provided by the users, and a new concrete fitness function for more accurate and effective quality evaluation of poems. The Turing test and fitness function contrast experiments show that the proposed algorithm could generate poems using given keywords or verse and the poems generated by the algorithm receive higher ratings and recognition than the original poems written by a human. The experimental results demonstrate the effectiveness of the proposed algorithm and prove that this research can make practical and theoretical contributions.

References

[1] Bunte A., Li P., and Niggemann O., “Mapping Data Sets to Concepts Using Machine Learning and A Knowledge Based Approach,” in Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Madeira, pp. 430-437, 2018.

[2] Chang Y., Chen J., Qu C., and Pan T., “Intelligent Fault Diagnosis of Wind Turbines Via A Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels,” Renewable Energy, vol. 153, pp. 205-213, 2020.

[3] Concepción E., Gervás P., and Méndez G., “A Microservice-Based Architecture for Story Generation,” in Proceedings of the Microservices, Odense, pp. 1-2, 2017.

[4] Concepción E, Gervás P., and Méndez G., “Evolving the INES Story Generation System: from Single to Multiple Plot Lines,” in Proceedings of the 10th International Conference on Computational Creativity, North Carolina, pp. 121-128, 2019.

[5] Díaz-Agudo B., Gervás P., and Gonzalez-Calero P., “Poetry generation in COLIBRI,” in Proceedings of the 6th European Conference on Case-Based Reasoning, Aberdeen, pp. 73-87, 2002.

[6] Fernandez Molanes R., Amarasinghe K., Rodriguez-Andina J., and Manic M., “Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware,” IEEE Industrial Electronics Magazine, vol. 12, no. 2, pp. 36-49, 2018. Elitist Strategy of Genetic Algorithms for Writing Tang Poetry 609

[7] Gatt A. and Krahmer E., “Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation,” Journal of Artificial Intelligence Research, vol. 61, pp. 75- 170, 2018.

[8] Gervás P. and León C, “Creativity and Universality in Language,” in Lecture Notes in Morphogenesis, Springer, 2016.

[9] Hao S., Ge F., Li Y., and Jiang J., “Multisensor Bearing Fault Diagnosis Based on One- Dimensional Convolutional Long Short-Term Memory Networks,” Measurement, vol. 159, no. 107802, 2020.

[10] Hopkins J., Kiela D., “Automatically Generating Rhythmic Verse with Neural Networks,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, pp. 168-178, 2017.

[11] Jack B., “Poetry and Emotion,” The Lancet, vol. 391, no. 10122, pp. 732-733, 2018.

[12] Janssens O., Slavkovikj V., Vervisch B., Stockman K., Loccufier M., Verstockt S., Van de Walle R., and Van Hoecke S., “Convolutional Neuralnet work Based Fault Detection for Rotating Machinery,” Journal of Sound and Vibration, vol. 377, pp. 331-345, 2016.

[13] Li P., “Automatic Generation of Children’s Songs Based On Machine Statistic Learning,” International Journal of Emerging Technologies in Learning (IJET), vol. 13, no. 3, pp. 17-31, 2018.

[14] Liu D., Guo Q., Li W. and Lv J., “A Multi-Modal Chinese Poetry Generation Model,” in Proceedings of International Joint Conference on Neural Networks, Rio de Janeiro, pp. 1-8, 2018.

[15] Liu H., Zhou J., Zheng Y., Jiang W., and Zhang Y., ‘‘Fault Diagnosis of Rolling Bearings with Recurrent Neural Network-Based Autoencoders,” ISA Transactions, vol. 77, pp. 167-178, 2018.

[16] Liu Z., Jia Z., Vong C., Bu S., Han J., and Tang X., “Capturing High-Discriminative Fault Features for Electronics-Rich Analog System Via Deep Learning,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1213-1226, 2017.

[17] Mathew T., Genetic Algorithm, Indian Institute of Technology Bombay, Mumbai, 2012.

[18] Meng Z., Zhan X., Li J., and Pan Z., “An Enhancement Denoising Autoencoder for Rolling Bearing Fault Diagnosis,” Measurement, vol. 130, pp. 448-454, 2018.

[19] Miguel L., Lopez R., Torii A., Miguel L., and Beck A., “Robust Design Optimization of TMDS in Vehicle-Bridge Coupled Vibration Problems,” Engineering Structures, vol. 126, no. 1, pp. 703- 711, 2016.

[20] Mizrak C. and Esen I., “The Optimisation of Rail Vehicle Bogie Parameters with The Fuzzy Logic Method in Order to Improve Passenger Comfort During Passage over Bridges,” International Journal of Heavy Vehicle Systems, vol. 24, no. 2, pp. 113-139, 2017.

[21] Oliveira H., “Tra-la-Lyrics 2.0: Automatic Generation of Song Lyrics on a Semantic Domain,” Journal of Artificial General Intelligence, vol. 6, no. 1, pp. 87-110, 2015.

[22] Oliveira H., Hervás R., Díaz A., and Gervás P., “Multilingual Extension and Evaluation of A Poetry Generator,” Natural Language Engineering, vol. 23, no. 6, pp. 929-967, 2017.

[23] Reiter E. and Robert D., Building Natural Language Generation Systems, Cambridge University, New York, 2000.

[24] Schlegel U., Cakmak E., Buchmüller J., and Keim D., “G-Rap: Interactive Text Synthesis Using Recurrent Neural Network Suggestions,” in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Brügge, pp. 111-116, 2018.

[25] Shao H., Jiang H., Zhang H., and Liang T., ‘‘Electric Locomotive Bearing Fault Diagnosis Using A Novel Convolutional Deep Belief Network,” IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2727-2736, 2018.

[26] Shaout A. and Crispin B., “Streaming Video Classification Using Machine Learning,” The International Arab Journal of Information Technology, vol. 17, no. 4A, pp. 677-682, 2020.

[27] Siddiqui M., Wedemann R., and Jensen H., “Avalanches and Generalized Memory Associativity in a Network Model for Conscious and Unconscious Mental Functioning,” Physica A: Statistical Mechanics and its Applications, vol. 490, pp. 127-138, 2018.

[28] Suma T. and Kumara Swamy Y., “Email Classification Using Adaptive Ontologies Learning,” TELKOMNIKA, vol. 14, no. 4, pp. 1472-1479, 2016.

[29] Sun R., Yang Z., Yang L., Qiao B., Chen X., and Gryllias K., “Planetary Gearbox Spectral Modeling Based on The Hybrid Method of Dynamics and Lstm,” Mechanical Systems and Signal Processing, vol. 138, no. 106611, 2020.

[30] Tobing B. and Manurung R., “A Chart Generation System for Topical Metrical Poetry,” in Proceedings of the 6th International Conference on Computational Creativity, Park City, pp. 308-314, 2015.

[31] Wang X., Qin Y., Wang Y., Xiang S., and Chen H., “ReLTanh: An Activation Function with Vanishing Gradient Resistance for SAE-Based Dnns and its Application to Rotating Machinery Fault Diagnosis,” Neurocomputing, vol. 363, pp. 88-98, 2019.

[32] Wen L., Li X., Gao L., and Zhang Y., “Anew Convolutional Neural Network-Based Data- Driven Fault Diagnosis Method,” IEEE 610 The International Arab Journal of Information Technology, Vol. 18, No. 4, July 2021 Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5990-5998, 2018.

[33] Yang W., Cheng Y., He J., Hu W., and Lin X., “Research on Community Competition and Adaptive Genetic Algorithm for Automatic Generation of Tang Poetry,” Mathematical Problems in Engineering, vol. 2016, no. 1, pp. 1-7, 2016.

[34] Yi X., Li R., and Sun M., “Chinese Poetry Generation with A Salient-Clue Machanism,” in Proceedings of the 22nd Conference on Computational Natural Language Learning, Brussels, pp. 241-250, 2018.

[35] Yi X., Sun M., Li R., and Yang Z., “Chinese Poetry Generation with a Working Memory Model,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, pp. 4553-4559, 2018.

[36] Zhou J., Du Z., Yang Z., and Xu Z., “Dynamic Parameters Optimization of Straddle-Type Monorail Vehicles Based Multi Objective Collaborative Optimization Algorithm,” Vehicle System Dynamics, vol. 53, no. 3, pp. 357-376, 2020.

[37] Ziv Y., Kupermintz H., and Aviezer O., “The Associations Among Maternal Negative Control, Children’s Social Information Processing Patterns, and Teachers’ Perceptions of Children’s Behavior In Preschool,” Journal of Experimental Child Psychology, vol. 142, pp. 18-35, 2016. Wujian Yang received B.Sc. in civil engineering from Zhejiang University, and a M.Sc. in Computing Science from Zhejiang University. Currently, his research focuses on Algorithm. Wenyong Weng is an Associate Professor of Computer and Computing Science at Zhejiang University City College, China. He is currently pursuing a Ph.D. at Zhejiang University and His research interests include Computer Vision and Machine Learing. GuanLin Chen received his M.Sc. in Computing Science and Ph.D. degree in Signal Processing from Zhejiang University, China. He has been at Zhejiang University City College since 2005, and now is a Full Professor. His work is related to Remote Sensing. Zihang Jiang is a M.Sc. student in College of Computer Science and Technology, Zhejiang University, China. His research interests include Machine Learing and Natural Language Processing.