  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.
