Automatically configuring constraint satisfaction programs: A case study

Minton, Steven

doi:10.1007/bf00143877

Cited by 113 publications

(58 citation statements)

References 28 publications

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“…Many real-world problems can be modeled as optimisation or classification problems, so the issues discussed here are widely relevant. However, other domains, such as bioinformatics, control, constraint programming and games have already investigated forms of both automated algorithm/heuristic selection and generation [5,41,74,90]. We argue that algorithm/heuristics selection and generation are crucial for all types of domains in which many methods and/or parameters are available, but no clear methodology or criteria for choosing them are available.…”

Section: Final Remarksmentioning

confidence: 98%

“…Another idea is to use two evolutionary algorithms: one for problem solving and another one (a so-called metaevolutionary algorithm) to tune the first one [59]. Finally, a pioneering approach to the automated generation of heuristics can be found in the domain of constraint satisfaction [74]; where a system for generating reusable heuristics is presented.…”

Section: Hyper-heuristicsmentioning

confidence: 99%

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Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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Section: Final Remarksmentioning

confidence: 98%

Section: Hyper-heuristicsmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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“…One of the earliest examples of systems that attempt to generate constraint solvers tailored to a specific problem is the MULTI-TAC system [7], which configures and compiles a constraint solver for a specific set of problems. It is written in LISP and performs ad-hoc customisation of a base constraint solver limited to a few characteristics.…”

Section: Related Workmentioning

confidence: 99%

Dominion: An Architecture-Driven Approach to Generating Efficient Constraint Solvers

Balasubramaniam

Silva

Jefferson

et al. 2011

2011 Ninth Working IEEE/IFIP Conference on Software Architecture

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Abstract-Constraints are used to solve combinatorial problems in a variety of industrial and academic disciplines. However most constraint solvers are designed to be general and monolithic, leading to problems with efficiency, scalability and extensibility. We propose a novel, architecture-driven constraint solver generation framework called Dominion to tackle these issues. For any given problem, Dominion generates a lean and efficient solver tailored to that problem. In this paper, we outline the Dominion approach and its implications for software architecture specification of the solver.

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“…We plan to develop a system that can automatically learn the optimal planner configuration for a given domain and problem distribution in a manner analogous to Minton's Multi-TAC system (Minton, 1996). Such system would perform a search in the configuration space of the PbR planner proposing different initial plan generators, candidate sets of rewriting rules, and search methods.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Plan Optimization by Plan Rewriting

Ambite¹,

Knoblock²,

Minton³

Intelligent Techniques for Planning

Self Cite

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Planning by Rewriting (PbR) is a paradigm for efficient high-quality planning that exploits declarative plan rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing planning efficiency and plan quality, PbR offers a new anytime planning algorithm. The plan rewriting rules can be either specified by a domain expert or automatically learned. We describe a learning approach based on comparing initial and optimal plans that produces rules competitive with manually-specified ones. PbR is fully implemented and has been applied to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.

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Automatically configuring constraint satisfaction programs: A case study

Cited by 113 publications

References 28 publications

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Dominion: An Architecture-Driven Approach to Generating Efficient Constraint Solvers

Plan Optimization by Plan Rewriting

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