Program Induction, Complexity and Occam''s Razor

Focusing on genetic programming, this work explores evolving computable functions and critiques existing machine learning methods for their complexity limitations. It challenges the No Free Lunch theorem, emphasizing that simpler functions are more common, aligning with Occam's razor based on probability rather than simplicity. The text examines the impact of representation, genetic operators, and fitness functions, proposing a novel crossover operator that enhances program termination rates. It also discusses how effective representations can solve larger problems by evolving solutions from smaller instances.