Linear genetic programming (LGP) is a flavor of genetic programming that adopts linear representations for executable programs. These linear representations can consist of a sequence of imperative instructions, offering powerful capabilities for representing complex relations in a compact manner. Linear representations also conveniently facilitate the detection of structurally non-effective code, i.e., instructions whose execution do not alter the program’s outcome. This allows for the study of code neutrality, where mutations to program elements does not change the relationship represented by the program. Since its conception, LGP has witnessed exciting advancements in methodology design, real-world applications, and the study of evolutionary theory. This tutorial introduces the fundamentals of LGP, its various representations, genetic variation operators, and provide application examples. It also explores the utility of LGP in studying evolvability and robustness, intriguing properties of evolutionary systems resulting from neutrality.