L. Correia

Dropout-Inspired Strategies For Enhanced Exploration In Evolutionary Algorithms

It is empirically demonstrate that per-variable stochastic deactivation, termed Individual Dropout (IDrop), significantly outperforms a standard GA on single-objective problems with exploitable global structure and improves convergence toward the theoretical Pareto front in multi-objective settings.