Generating Local Search Neighborhood with Synthesized Logic Programs
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.
Cite
@article{arxiv.1909.08242,
title = {Generating Local Search Neighborhood with Synthesized Logic Programs},
author = {Mateusz Ślażyński and Salvador Abreu and Grzegorz J. Nalepa},
journal= {arXiv preprint arXiv:1909.08242},
year = {2019}
}
Comments
In Proceedings ICLP 2019, arXiv:1909.07646