A semantic genetic programming framework based on dynamic targets

摘要

Semantic GP is a promising branch of GP that introduces semantic awareness during genetic evolution to improve various aspects of GP. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors of previous runs. To obtain the final solution, SGP-DT combines the solutions of each run using linear scaling. SGP-DT presents a new methodology to produce the offspring that does not rely on the classic crossover. The synergy between such a methodology and linear scaling yields final solutions with low approximation error and computational cost. We evaluate SGP-DT on eleven well-known data sets and compare with epsilon-lexicase, a state-of-the-art evolutionary technique, and seven Machine Learning techniques. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of epsilon-lexicase. Tuning SGP-DT 's configuration greatly reduces the computational cost while still obtaining competitive results.