Controlling code growth by dynamically shaping the genotype size distribution

摘要

Genetic programming is a hyperheuristic optimization approach that seeks to evolve various forms of symbolic computer programs, in order to solve a wide range of problems. However, the approach can be severely hindered by a significant computational burden and stagnation of the evolution caused by uncontrolled code growth. This paper introduces HARM-GP, a novel operator equalization method that conducts an adaptive shaping of the genotype size distribution of individuals in order to effectively control code growth. Its probabilistic nature minimizes the computational overheads on the evolutionary process while its generic formulation allows it to remain independent of both the problem and the genetic variation operators. Comparative results over twelve problems with different dynamics, and over nine other algorithms taken from the literature, show that HARM-GP is excellent at controlling code growth while maintaining good overall performance. Results also demonstrate the effectiveness of HARM-GP at limiting overfitting in real-world supervised learning problems.