ACORg: A Gradient-Guided ACO Algorithm for Neural Network Learning

The ACO(R) algorithm is an Ant Colony Optimization (ACO) algorithm for real-valued optimization, and has been applied to neural network learning. Unlike many algorithms for neural network learning, ACO(R) does not use gradient information at all in its operation. Also, unlike many discrete ACO algorithms, ACO(R) does not allow for the incorporation of domain-specific heuristics. In this work, we present a gradient-guided variation of ACO(R), that we call ACO(Rg), that incorporates gradient information while retaining the core aspects of the ACO(R) algorithm. Experimental results using 10-fold cross-validation with 20 UCI datasets indicate that our variation produces lower test set error than standard ACO(R), after a markedly smaller number of training generations.