Generating Local Search Neighborhood with Synthesized Logic Programs
This work addresses the need for customized neighborhood operators in Local Search meta-heuristics, offering a domain-specific approach that is incremental in nature.
The paper tackled the problem of generating tailored Local Search neighborhoods for discrete optimization problems by introducing Noodle, a logic programming-based framework, and demonstrated its ability to synthesize efficient operators for the traveling salesman problem, reproducing known results.
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.