Evolving an edge selection formula for ant colony optimization

Runka, Andrew

doi:10.1145/1569901.1570046

Cited by 20 publications

(16 citation statements)

References 6 publications

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“…This has the potential to produce a much better variation of the original humandesigned algorithm. Runka (2009) presents such a system for evolving the edge selection formula of an ant colony optimisation algorithm. While the evolved formulae were tested on only two unseen travelling salesman problem instances, the results were better than the original humandesigned formula.…”

Section: Travelling Salesman Problemmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

976

550

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

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Section: Travelling Salesman Problemmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

976

550

View full text Add to dashboard Cite

show abstract

“…In the case of generation methods, hyper-heuristics search the space of heuristics constructed from components rather than well-defined heuristics. The examples where generation hyper-heuristics were used include several domains: timetabling and scheduling [24], the traveling salesman problem [25,26] or cutting and packing [27][28][29].…”

Section: Hyper-heuristics and Their Classificationmentioning

confidence: 99%

Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Świercz¹

2017

Heuristics and Hyper-Heuristics - Principles and Applications

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Many decision and optimization problems arising in bioinformatics field are time demanding, and several algorithms are designed to solve these problems or to improve their current best solution approach. Modeling and implementing a new heuristic algorithm may be time-consuming but has strong motivations: on the one hand, even a small improvement of the new solution may be worth the long time spent on the construction of a new method; on the other hand, there are problems for which good-enough solutions are acceptable which could be achieved at a much lower computational cost. In the first case, specially designed heuristics or metaheuristics are needed, while the latter hyper-heuristics can be proposed. The paper will describe both approaches in different domain problems.

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“…White and Salehi-Abari [21] proposed an ACO-inspired crossover operation for GP. Runka [22] applied the natural evolutionary mechanism of Genetic Programming (GP) to build an improved formula for an ACO algorithm. Although a hybrid approach to credit scoring is not new, none of the reported work has done a performance comparison of various AI techniques using complete sets of benchmark data as described in this paper.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study

Aliehyaei

2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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Credit scoring is a commonly used method for evaluating the risk involved in granting credits. Both Genetic Programming (GP) and Ant Colony Optimization (ACO) have been investigated in the past as possible tools for credit scoring. This paper reports an investigation into the relative performances of GP, ACO and a new hybrid GP-ACO approach, which relies on the ACO technique to produce the initial populations for the GP technique. Performance of the hybrid approach has been compared with both the GP and ACO approaches using two well-known benchmark data sets. Experimental results demonstrate the dependence of GP and ACO classification accuracies on the input data set. For any given data set, the hybrid approach performs better than the worse of the other two methods. Results also show that use of ACO in the hybrid approach has only a limited impact in improving GP performance.

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Evolving an edge selection formula for ant colony optimization

Cited by 20 publications

References 6 publications

Hyper-heuristics: a survey of the state of the art

Hyper-heuristics: a survey of the state of the art

Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study

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