We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling
problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and …

Symbolic regression is an important method of data-driven modeling, which can provide
explicit mathematical expressions for data analysis. However, the existing genetic …

Multitask genetic programming methods have been applied to various domains, such as
classification, regression, and combinatorial optimization problems. Most existing multitask …

Dynamic job shop scheduling has a wide range of applications in reality such as order
picking in warehouse. Using genetic programming to design scheduling heuristics for …

Linear genetic programming (LGP) has been successfully applied to various problems such
as classification, symbolic regression and hyper-heuristics for automatic heuristic design. In …

Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world
applications. Genetic programming is a promising technique to solve DJSS, which …

Classification is a supervised machine learning process that categories an instance based
on a number of features. The process of classification involves several stages, including …

Ensemble learning is one of the most powerful extensions for improving upon individual
machine learning models. Rather than a single model being used, several models are …

This paper introduces a novel evolutionary approach which can be applied to supervised,
semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution …

In classification, machine learning algorithms can suffer a performance bias when data sets
are unbalanced. Binary data sets are unbalanced when one class is represented by only a …