Mark Johnston

TL;DR: Four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments are developed.

TL;DR: Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, naive Bayes, and support vector machines, on highly unbalanced tasks.

TL;DR: Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP).

TL;DR: A new approach to automatic programming via iterated local search (APRILS) for dynamic job shop scheduling and suggests that the good performance of APRILS comes from the balance between exploration and exploitation in its search mechanism.

TL;DR: This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data and empirically show that these new Fitness functions evolve classifiers with good performance on both the minority and majority classes.