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- Edmund K. Burke, Michel Gendreau, +4 authors Rong Qu
- JORS
- 2013

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… (More)

- John H. Drake, Nikolaos Kililis, Ender Özcan
- EuroGP
- 2013

The vehicle routing problem (VRP) is a family of problems whereby a fleet of vehicles must service the commodity demands of a set of geographically scattered customers from one or more depots, subject to a number of constraints. Early hyper-heuristic research focussed on selecting and applying a low-level heuristic at a given stage of an optimi-sation… (More)

Memetic algorithms (MAs) are meta-heuristics that join genetic algorithms with hill climbing. MAs have recognized success in solving difficult search and optimization problems. Hyperheuris-tics are proposed as an alternative to meta-heuristics. A hyperheuristic is a mechanism that chooses a heuristic from a set of heuristics, applies it to a candidate… (More)

- Ender Özcan, Murat Yilmaz
- ICANNGA
- 2007

In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using a random walk and a hill climbing components. Furthermore, one of the… (More)

- Mashael Maashi, Ender Özcan, Graham Kendall
- Expert Syst. Appl.
- 2014

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three… (More)

- Anas Elhag, Ender Özcan
- Expert Syst. Appl.
- 2015

Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimized. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than… (More)

- Ender Özcan, Burak Bilgin, Emin Erkan Korkmaz
- Intell. Data Anal.
- 2008

Meta-heuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a meta-heuristic for solving a problem in a certain domain. Hyper-heuristics introduce a novel approach for… (More)

- Edmund K. Burke, Graham Kendall, Mustafa Misir, Ender Özcan
- Annals OR
- 2012

Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so… (More)

- Burak Bilgin, Ender Özcan, Emin Erkan Korkmaz
- PATAT
- 2006

Hyper-heuristics are proposed as a higher level of abstraction as compared to the metaheuristics. Hyper-heuristic methods deploy a set of simple heuristics and use only nonproblem-specific data, such as, fitness change or heuristic execution time. A typical iteration of a hyper-heuristic algorithm consists of two phases: heuristic selection method and move… (More)

In evolutionary algorithms, crossover is used to recombine two candidate solutions to yield a new solution which hopefully inherits good material from both. Hyper-heuristics are high-level search method-ologies which operate on a search space of heuristics. Hyper-heuristics can be broadly split into two categories; heuristic selection and generation… (More)