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Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results.

Purpose—finding ways of improving learning in a formal higher education context. Design/methodology/approach—in the proposed model we will consider extending traditional content management systems, giving learners the possibility to add new materials and to rate them, and a hybrid strategy that combines technical recommendations with some profile-based filtering to offer adaptive and suitable sequencing learning content to learners. Findings—the experiment shows that our recommendation techniques are able to reflect the learners’ interests. Research limitations/implications—it’s necessary to demonstrate the contributions of these kinds of solutions to the learners’ success. Practical implications—theoretical and practical framework for future research in the field was developed. Originality/Value—the main contributions are the extended Learning Management System and the hybrid Recommender System, which implements a new proposal to evaluate learners’ similarities. Research type: research paper.