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.",