Exploring Elitism Strategies in Nested Tournament Selection for Multi-Objective Genetic Programming
TL;DR
This study systematically investigates multiple elitism mechanisms for NT within tree-based MO Genetic Programming (MOGP), including NSGA-II population replacement, crowding distance, first-objective, and a novel ideal-point strategy, comparing them against non-elitist NT and standard NSGA-II.
Abstract
Nested Tournament (NT) is a Multi-Objective (MO) selection method that enables fine-grained control of selection pressure through sequential single-objective tournaments. Although previously proposed, the impact of elitism strategies within NT remains largely unexplored. This study systematically investigates multiple elitism mechanisms for NT within tree-based MO Genetic Programming (MOGP), including NSGA-II population replacement, crowding distance, first-objective, and a novel ideal-point strategy, comparing them against non-elitist NT and standard NSGA-II. Experiments are conducted with up to five objectives for the accuracy-complexity trade-off and show that elitism design critically influences stability, efficiency, and semantic diversity. Notably, simpler NT-specific elitism strategies achieve comparable performance to NSGA-II at lower computational cost while better preserving semantic diversity. Overall, the findings highlight NT as an efficient MOGP alternative to selection methods based on Pareto Fronts.