Research Report on Automatic Synthesis of Local Search Neighborhood Operators

TL;DR

This paper introduces a method to automatically synthesize local search neighborhood operators from problem structure in constraint programming, using a declarative language and grammar evolution for improved heuristic search.

Contribution

It presents a novel approach combining a Neighborhood Definition Language and grammar evolution to automatically generate efficient local search operators based on problem models.

Findings

Automated synthesis of neighborhood operators improves search efficiency.

The approach leverages a declarative language for fine-grained operator specification.

Grammar evolution enables automatic adaptation of operators to different problems.

Abstract

Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.