HyFlex: A Benchmark Framework for Cross-domain Heuristic Search (1107.5462v1)

Abstract: Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. In order to make these methods more generally applicable, it is important to eliminate or reduce the role of the human expert in the process of designing an effective methodology to solve a given computational search problem. Researchers developing such methodologies are often constrained on the number of problem domains on which to test their adaptive, self-configuring algorithms; which can be explained by the inherent difficulty of implementing their corresponding domain specific software components. This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems, and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge the problem domains, and thus can concentrate his/her efforts in designing adaptive general-purpose heuristic search algorithms. Four hard combinatorial problems are fully implemented (maximum satisfiability, one dimensional bin packing, permutation flow shop and personnel scheduling), each containing a varied set of instance data (including real-world industrial applications) and an extensive set of problem specific heuristics and search operators. The framework forms the basis for the first International Cross-domain Heuristic Search Challenge (CHeSC), and it is currently in use by the international research community. In summary, HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed, and reliably compared.

Overview of HyFlex: A Benchmark Framework for Cross-domain Heuristic Search

The paper introduces HyFlex, a benchmark framework that presents a novel approach towards advancing cross-domain heuristic search methodologies. Recognizing a widespread challenge in heuristic search—namely, the dependency on domain-specific knowledge—the authors propose a sophisticated system that abstracts algorithmic components from problem-specific details, thereby shifting the focus towards developing adaptive general-purpose search algorithms.

Framework Description

HyFlex is designed to facilitate the development and empirical testing of heuristic methods across multiple problem domains without an exhaustive understanding of each individual domain. This modular Java-based framework supports various combinatorial optimization problems with built-in domain-specific components, such as solution representation, fitness evaluation, instance data, and a repertoire of problem-specific heuristics.

The uniqueness of HyFlex lies in its ability to act as a comprehensive benchmark, offering not only instance data but also domain-specific functionalities, thereby reducing the barriers for researchers focusing solely on algorithmic design. By doing so, HyFlex fosters innovation in designing universal heuristic approaches, propelling the field toward greater autonomy from domain experts.

Problem Domains and Heuristics

HyFlex encompasses four complex combinatorial optimization problems: MAX-SAT, one-dimensional bin packing, permutation flow shop, and personnel scheduling. Each domain is equipped with a suite of heuristics categorized as mutational, ruin-recreate, local search, and crossover, ensuring a broad and realistic experimental evaluation capability.

1. MAX-SAT: Solutions are evaluated based on the number of unsatisfied clauses. Heuristics include variations of GSAT and WalkSAT.
2. Bin Packing: The primary goal is minimizing the number of bins used. Fitness evaluation deviates from traditional methods to circumvent large plateaus near optimality.
3. Permutation Flow Shop: Objectives focus on minimizing makespan with heuristics such as NEH and mutational operators.
4. Personnel Scheduling: Involves creating feasible rosters, emphasizing constraints' satisfaction and employee preferences.

Comparative Algorithms

The paper exemplifies HyFlex's utility through a comparative analysis of three heuristic-based algorithms: Iterated Local Search (ILS), Tabu Search Hyper-heuristic with Adaptive Acceptance (TS-AA), and a Memetic Algorithm (MA). Each approach demonstrates distinctive strengths across different domains, underscoring the complexity and variability intrinsic to cross-domain heuristic development.

• ILS excels with a perturbation-improvement cycle, proving highly effective in problem domains like bin packing.
• TS-AA leverages a heuristic valuation strategy combined with adaptive acceptance, showcasing remarkable performance in scheduling-related problems.
• MA illustrates the potentials of population-based approaches, although its performance varies significantly by domain.

Implications and Future Directions

HyFlex exemplifies a strategic shift towards developing versatile heuristic algorithms with the potential to operate effectively across varied problem domains. This approach holds the promise of reducing algorithm development times and widening the applicability of heuristic methods, aligning closely with demands for adaptable and scalable AI solutions.

The continued adoption and development of frameworks like HyFlex not only push the boundaries of heuristic design but also pave the way for future innovations in automated problem-solving methodologies. Future extensions might include additional problem domains, multi-objective optimizations, and real-time adaptability, further solidifying HyFlex's role as a pivotal tool for researchers worldwide.

In summary, this paper contributes a significant advancement in heuristic search methodology, equipping researchers with the tools to innovate without the constraints traditionally imposed by problem-specific component development.