On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
This work addresses an incremental understanding gap in genetic programming dynamics for researchers and practitioners in heuristic optimization.
The study investigated the effectiveness of different crossover operators in genetic programming with offspring selection on benchmark problems, finding that standard sub-tree swapping crossover is a good default choice and that random selection of operators can improve best solution quality without size constraints.
Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve among others regression, classification, and time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.