Abstract Space, while inherent to the natural world, often finds itself omitted in bio-inspired computational system designs. Spatial genetic programming (SGP) is a GP paradigm that includes space as a fundamental dimension to evolve along with linear genetic programming programs. Here, the spatial blueprint dictates the program’s execution sequence. Despite prior SGP iterations showcasing good performance in solving decision-making problems, the complete spatial mode’s efficacy and the consequential spatial ramifications remain ambiguous. This study embarks on a two-pronged approach: We start with SGP’s application to a comprehensive subset of symbolic regression problems, taken from Feynman’s physics lectures, then deepen the study by an extensive analysis to show the spatial dimension’s impact on the evolution of SGP models. Our intention is to primarily focuses on the spatial dimension’s influence on generational diversity and the emergence of spatially-induced localization within the system. We propose a suite of spatial evolutionary operators that should offer insights into leveraging SGP as a tool to examine spatial impacts within problem-solving techniques. Preliminary results indicate that spatial constructs can indeed serve as a leverage to enhance the evolution of better models.
Miralavy et al. (2026) studied this question.