Generating Local Search Neighborhood with Synthesized Logic Programs

TL;DR

This paper introduces Noodle, a logic programming framework that synthesizes customized local search neighborhoods for discrete optimization problems, demonstrated through experiments on the traveling salesman problem.

Contribution

The paper presents a novel framework combining logic programming and genetic programming to automatically generate problem-specific local search neighborhoods.

Findings

Synthesized neighborhoods for TSP, some matching known effective operators.

Demonstrated the feasibility of automated neighborhood generation.

Preliminary results show promising performance improvements.

Abstract

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.