Memory augmented hyper-heuristic framework to solve multi-disciplinary problems inspired by cognitive problem solving skills

This paper proposes a new framework, named Deja-Vu+, which is an extension of Deja Vu framework, a classic study on hyper-heuristic framework with 2R (Record and Recall) modules. Deja-Vu+ has the ability to handle two other domains, namely regression and unsupervised learning. The extension examines the strength of Deja-Vu+ for solving regression and unsupervised learning tasks. The regression problems are treated here as multiclass classification tasks, and unsupervised learning tasks are considered as clustering problems. The proposed framework is tested on a number of regression and unsupervised learning benchmark problems and has shown promising results to handle regression as classification. The framework attains an overall average accuracy of 70% for regression and clustering data sets. Deja-Vu+ is knowledge-rich hyper-heuristic framework, which is capable enough to transfer knowledge successfully. This knowledge transfer improves the performance of learning by avoiding the extensive heuristic search process. Our experimental results show that using previously attained knowledge to reduce the computational effort is beneficial in solving multi-disciplinary machine learning problems.