Dispatching rules for production scheduling: a hyper-heuristic landscape analysis

Authors:
Gabriela Ochoa;José Antonio Vázquez-Rodríguez;Sanja Petrovic;Edmund Burke
Affiliations:
Automated Scheduling, optimisation and Planning Research Group, School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK;Automated Scheduling, optimisation and Planning Research Group, School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK;Automated Scheduling, optimisation and Planning Research Group, School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK;Automated Scheduling, optimisation and Planning Research Group, School of Computer Science and Information Technology, University of Nottingham, Nottingham, UK
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 6
Cited 3

New search spaces for sequencing problems with application to job shop scheduling

Management Science
Evolution based learning in a job shop scheduling environment

Computers and Operations Research - Special issue on genetic algorithms
A Promising Genetic Algorithm Approach to Job-Shop SchedulingRe-Schedulingand Open-Shop Scheduling Problems

Proceedings of the 5th International Conference on Genetic Algorithms
Crossover, Macromutationand, and Population-Based Search

Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

Evolutionary Computation
Scheduling: Theory, Algorithms, and Systems

Scheduling: Theory, Algorithms, and Systems

Frequency distribution based hyper-heuristic for the bin-packing problem

EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators

Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Genetic Programming and Evolvable Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hyper-heuristics or "heuristics to chose heuristics" are an emergent search methodology that seeks to automate the process of selecting or combining simpler heuristics in order to solve hard computational search problems. The distinguishing feature of hyper-heuristics, as compared to other heuristic search algorithms, is that they operate on a search space of heuristics rather than directly on the search space of solutions to the underlying problem. Therefore, a detailed understanding of the properties of these heuristic search spaces is of utmost importance for understanding the behaviour and improving the design of hyper-heuristic methods. Heuristics search spaces can be studied using the metaphor of fitness landscapes. This paper formalises the notion of hyper-heuristic landscapes and performs a landscape analysis of the heuristic search space induced by a dispatching-rule-based hyper-heuristic for production scheduling. The studied hyper-heuristic spaces are found to be "easy" to search. They also exhibit some special features such as positional bias and neutrality. It is argued that search methods that exploit these features may enhance the performance of hyper-heuristics.