Mapping the performance of heuristics for Constraint Satisfaction

Ortíz-Bayliss, José Carlos; Özcan, Ender; Parkes, Andrew J.; Terashima-Marín, Hugo

doi:10.1109/cec.2010.5585965

Cited by 19 publications

(14 citation statements)

References 19 publications

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“…In complete search methods for solving CSPs, heuristics are usually designed based on the fail-first principle [19] which is based on the idea of selecting the variable with the highest probability of failure. Previous studies have shown that a heuristic may work well for some classes of instances, but perform badly for others [33]. Nine human-designed variable ordering heuristics are used in this investigation and they are described below:…”

Section: Variable Ordering Heuristicsmentioning

confidence: 99%

Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

Sosa-Ascencio

Ochoa

Terashima-Marín

et al. 2015

Genet Program Evolvable Mach

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We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results.

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Section: Variable Ordering Heuristicsmentioning

confidence: 99%

Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

Sosa-Ascencio

Ochoa

Terashima-Marín

et al. 2015

Genet Program Evolvable Mach

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“…add ( new RPHC< S t a t e >( l o c a l i t y ) ) ; return r e s u l t ; } } itates a wide variety of hyperheuristic strategies. In particular, the approaches adopted in [27], [28] and [29] may all be considered as specic congurations of Hyperion components.…”

Section: { L I S T < M E T a H E U R I S T I C < S T A T E > > Lm = Imentioning

confidence: 99%

Hyperion – A Recursive Hyper-Heuristic Framework

Swan

Özcan

Kendall

2011

Lecture Notes in Computer Science

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Abstract. Hyper-heuristics are methodologies used to search the space of heuristics for solving computationally dicult problems. We describe an object-oriented domain analysis for hyper-heuristics that orthogonally decomposes the domain into generative policy components. The framework facilitates the recursive instantiation of hyper-heuristics over hyper-heuristics, allowing further exploration of the possibilities implied by the hyper-heuristic concept. We describe Hyperion, a Java TM class library implementation of this domain analysis.

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“…These quadrants, and the instances in set A which correspond to each one of them are shown in Fig 8. Even though these features seem very simple, there is evidence that they can be used to properly map one the problem state to some suitable heuristics [39].…”

Section: A Exploiting the Problem Statementioning

confidence: 99%

Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space

Ortíz-Bayliss

Terashima-Marín

Özcan

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-Variable ordering has been a recurrent topic of study in the field of constraint satisfaction because of its impact in the cost of the search. Various variable ordering heuristics have been proposed to help guiding the search under different situations. One important direction of the study about variable ordering is the use of distinct heuristics as the search progresses to reduce the cost of the search. Even though the idea of combining heuristics goes back to the 60's, only a few works that study which heuristics to use and how they interact with each other have been described. In this investigation, we analyse the interactions of four important variable ordering heuristics by combining them through hyper-heuristics that decide the heuristic to apply based on the depth of the nodes in the search tree. The paper does not include any specific model for generating such hyperheuristics; instead, it presents an analysis of the changes in the cost when different heuristics are applied during the search by using one simple hyper-heuristic representation. The results show that selectively applying distinct heuristics as the search progresses may lead to important reductions in the cost of the search with respect to the performance of the same heuristics used in isolation.

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Mapping the performance of heuristics for Constraint Satisfaction

Cited by 19 publications

References 19 publications

Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

Hyperion – A Recursive Hyper-Heuristic Framework

Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space

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