  This paper presents a Genetic Programming (GP) approach to solving
multi-robot path planning (MRPP) problems in single-lane workspaces,
specifically those easily mapped to graph representations. GP's versatility
enables this approach to produce programs optimizing for multiple attributes
rather than a single attribute such as path length or completeness. When
optimizing for the number of time steps needed to solve individual MRPP
problems, the GP constructed programs outperformed complete MRPP algorithms,
i.e. Push-Swap-Wait (PSW), by $54.1\%$. The GP constructed programs also
consistently outperformed PSW in solving problems that did not meet PSW's
completeness conditions. Furthermore, the GP constructed programs exhibited a
greater capacity for scaling than PSW as the number of robots navigating within
an MRPP environment increased. This research illustrates the benefits of using
Genetic Programming for solving individual MRPP problems, including instances
in which the number of robots exceeds the number of leaves in the tree-modeled
workspace.
