A new crossover mechanism for genetic algorithms for tree optimization problems
Genetic Algorithms (GAs) have been widely applied in Steiner tree optimization problems. However, as the core operation, existing crossover operators for tree-based GAs suffer from producing illegal offspring trees. Therefore, some global link information must be adopted to ensure the connectivity of the offspring, which incurs heavy computation. To address this problem, this paper proposes a new crossover mechanism, called Leaf Crossover, which generates legal offspring by just exchanging partial parent chromosomes, requiring neither the global network link information, encoding/decoding nor repair operations. Our simulation study indicates that GAs with leaf crossover outperform GAs with existing crossover mechanisms in terms of not only producing better solutions but also converging faster in networks of varying sizes.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Zhang, Q., Yang, S., Liu, M., Liu, J. and Jiang L. (2020) A new crossover mechanism for genetic algorithms for tree optimization problems. IEEE Transactions on Cybernetics,
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes