Robopol Refined: A Universal Guided Search Engine for Combinatorial Optimization

Robopol Refined is a reusable guided search engine for combinatorial optimization. The paper presents the method as a universal framework built around a generic search core and problem-specific adapters. The core handles candidate management, beam-style selection, repeated improvement, perturbation, deterministic parallel search, and diagnostics, while adapters supply scoring, move generation, guidance edges, local search, and domain-specific validity logic. The document focuses on the public architecture and positioning of Robopol Refined across routing, scheduling, packing, graph, assignment, and subset-selection problems. It intentionally avoids disclosing proprietary implementation details such as exact edge ranking formulas, candidate scoring rules, perturbation schedules, parameter recipes, or benchmark-specific tuning.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-18
DOI
https://doi.org/10.5281/zenodo.20273854
Primary Topic
Constraint Satisfaction and Optimization
Type
preprint
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preprint

Robopol Refined: A Universal Guided Search Engine for Combinatorial Optimization

Robert Polak
Zenodo (CERN European Organization for Nuclear Research)
Constraint Satisfaction and Optimization
preprint

Robopol Refined: A Universal Guided Search Engine for Combinatorial Optimization

Robert Polak
preprint en

Abstract

Robopol Refined is a reusable guided search engine for combinatorial optimization. The paper presents the method as a universal framework built around a generic search core and problem-specific adapters. The core handles candidate management, beam-style selection, repeated improvement, perturbation, deterministic parallel search, and diagnostics, while adapters supply scoring, move generation, guidance edges, local search, and domain-specific validity logic. The document focuses on the public architecture and positioning of Robopol Refined across routing, scheduling, packing, graph, assignment, and subset-selection problems. It intentionally avoids disclosing proprietary implementation details such as exact edge ranking formulas, candidate scoring rules, perturbation schedules, parameter recipes, or benchmark-specific tuning.

Zenodo (CERN European Organization for Nuclear Research)
Constraint Satisfaction and Optimization
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