Elitist Strategy of Genetic Algorithms for Writing
Tang Poetry
Wujian
Yang1, Wenyong Weng1, Guanlin Chen1, and
Zihang Jiang2
1Department of Computer and Computing Science,
Zhejiang University City College, China
2College
of Computer Science and Technology, Zhejiang University, China
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.
Keywords: Elitist strategy, adaptive genetic algorithm,
automatic generation, tang poetry, self-help writing.
Received October 24, 2019;
accepted December 15, 2020