Existing genetic programming (GP) methods are typically designed based on a
certain representation, such as tree-based or linear representations. These
representations show various pros and cons in different domains. However, due
to the complicated relationships among representation and fitness landscapes of
GP, it is hard to intuitively determine which GP representation is the most
suitable for solving a certain problem. Evolving programs (or models) with
multiple representations simultaneously can alternatively search on different
fitness landscapes since representations are highly related to the search space
that essentially defines the fitness landscape. Fully using the latent
synergies among different GP individual representations might be helpful for GP
to search for better solutions. However, existing GP literature rarely
investigates the simultaneous effective use of evolving multiple
representations. To fill this gap, this paper proposes a multi-representation
GP algorithm based on tree-based and linear representations, which are two
commonly used GP representations. In addition, we develop a new
cross-representation crossover operator to harness the interplay between
tree-based and linear representations. Empirical results show that navigating
the learned knowledge between basic tree-based and linear representations
successfully improves the effectiveness of GP with solely tree-based or linear
representation in solving symbolic regression and dynamic job shop scheduling
problems.

本文提出了一种基于树形和线性表示的多表示遗传编程算法，通过跨表示交叉运算符，成功改善了仅使用树形或线性表示的遗传编程在符号回归和动态车间调度问题中的效果。