Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Ant Colony Optimization
Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Examination timetabling using late acceptance hyper-heuristics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ant based hyper heuristics with space reduction: a case study of the p-median problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A genetic programming based hyper-heuristic approach for combinatorial optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Automating the packing heuristic design process with genetic programming
Evolutionary Computation
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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Convergence proofs for ant colony optimization are limited [1], only in some cases it is possible to assure that the algorithm will find an optimal solution. It is even more difficult to state how long it will take, but it has been found experimentally that the computing time increases at least exponentially with the size of the problem [2]. To overcome this, the concept of hyper-heuristics could be applied. The idea behind hyper-heuristics is to find some combination of simple heuristics to solve a problem instead than solving it directly. In this paper we introduce the first attempt to combine hyper-heuristics with an ACO algorithm. The resulting algorithm was applied to the two-dimensional bin packing problem, and encouraging results were obtained when solving classic instances taken from the literature. The performance of our approach is always equal or better than that of any of the simple heuristics studied, and comparable to the best metaheuristics known.