- author = "Jinghui Zhong and Linhao Li and Wei-Li Liu and Liang Feng and Xiao-Min Hu",
- title = "A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "2665--2672",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Coevolutionary Algorithm, Transfer Learning, Evolutionary Computation",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790352",
- size = "8 pages",
- abstract = "Cartesian Genetic Programming (CGP) is a powerful and popular tool for automatic generation of computer programs to solve user defined tasks. This paper proposes a Co-evolutionary CGP (named Co-CGP) which can automatically gain high-order knowledge to accelerate the search. In the Co-CGP, two modules are working in cooperation to solve a given problem. One module focuses on solving a series of small scale problems of the same type to generate the building blocks. Simultaneously, the second module focuses on combing the available building blocks to construct the final solution. Besides, an adaptive control strategy is introduced to automatically evaluate the effectiveness of the building blocks and adjust the search behaviour adaptively so as to improve search efficiency. The proposed Co-CGP is tested on eight problems with different complexities. Experimental results show that the Co-CGP can significantly improve the performance of CGP, in terms of both search efficiency and accuracy.",
-
notes = "
IEEE Catalog Number: CFP19ICE-ART",
