GP vs GI
John Robert Woodward, Colin G. Johnson, Alexander E. I. Brownlee · 2016
Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with existing software, and therefore immediately has the aim of tackling real software. As evolution is the main approach to GI to manipulating programs, this connection with real software should persuade the GP community to confront the issues around what it originally set out to tackle i.e. evolving real software.