Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A Study on the use of "self-generation'' in memetic algorithms
Natural Computing: an international journal
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A hybrid grouping genetic algorithm for the registration area planning problem
Computer Communications
Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A new grouping genetic algorithm approach to the multiple traveling salesperson problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Memetic Algorithm for the Delineation of Local Labour Markets
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Adaptive evolutionary algorithms for the delineation of local labour markets
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Expert Systems with Applications: An International Journal
Enhanced genetic algorithm with guarantee of feasibility for the unit commitment problem
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Finding Feasible Timetables Using Group-Based Operators
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time.