Searching the Hyper-heuristic Design Space

Swan, Jerry; Woodward, John R.; Özcan, Ender; Kendall, Graham; Burke, Edmund K.

doi:10.1007/s12559-013-9201-8

Cited by 33 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GP may find out at some point of evolution that using a linear model is profitable, but does not 'sanction, mandate or suggest' it. The detection of linear relationships between features is of course a relatively trivial computational task, but the ability to explicitly recognize such patterns and inject the corresponding (sub)programs into the population can be viewed as a special case of a more general recognition-exploitation strategy [13].…”

Section: Discussionmentioning

confidence: 99%

Pattern-guided genetic programming

Krawiec

Swan

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Online progress in search and optimization is often hindered by neutrality in the fitness landscape, when many genotypes map to the same fitness value. We propose a method for imposing a gradient on the fitness function of a metaheuristic (in this case, Genetic Programming) via a metric (Minimum Description Length) induced from patterns detected in the trajectory of program execution. These patterns are induced via a decision tree classifier. We apply this method to a range of integer and boolean-valued problems, significantly outperforming the standard approach. The method is conceptually straightforward and applicable to virtually any metaheuristic that can be appropriately instrumented.

show abstract

Section: Discussionmentioning

confidence: 99%

Pattern-guided genetic programming

Krawiec

Swan

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…This leads to the conclusion that the hybrid acceptance scheme approximates the actions of a higher level iterated local search while maintaining a balance between intensification and diversification. This is in-line with extracting domain independent domain knowledge as discussed in [53] where the knowledge encapsulates the heuristic indexes assigned to each acceptance mechanism. We have seen in Section 5.3 that the value t s = 500 msec, results in slightly better objective function values, especially when compared to t s = nil.…”

Section: An Analysis Of Tebha-hhmentioning

confidence: 99%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta

Özcan

2015

Information Sciences

Self Cite

View full text Add to dashboard Cite

Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

show abstract

“…Other works related to this investigation deal with the understanding of how heuristics work when applied in a collaborative manner [38] and the analysis of the performance of heuristics on different regions of the landscape [34,40]. There is also a growing interest in applying hyper-heuristic to solve dynamic [35] and multiobjective problems [18].…”

Section: Hyper-heuristicsmentioning

confidence: 99%

A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems

2015

View full text Add to dashboard Cite

Constraint satisfaction problems represent an important topic of research due to their multiple applications in various areas of study. The most common way to solve this problem involves the use of heuristics that guide the search into promising areas of the space. In this article, we present a novel way to combine the strengths of distinct heuristics to produce solution methods that perform better than such heuristics on a wider range of instances. The methodology proposed produces neural networks that represent hyper-heuristics for variable ordering in constraint satisfaction problems. These neural networks are generated and trained by running a genetic algorithm that has the task of evolving the topology of the networks and some of their learning parameters. The results obtained suggest that the produced neural networks represent a feasible alternative for coding hyper-heuristics that control the use of different heuristics in such a way that the cost of the search is minimized.

show abstract

Searching the Hyper-heuristic Design Space

Cited by 33 publications

References 38 publications

Pattern-guided genetic programming

Pattern-guided genetic programming

A tensor-based selection hyper-heuristic for cross-domain heuristic search

A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems

Contact Info

Product

Resources

About