Towards a theory of emergent functionality
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Applying evolutionary programming to selected traveling salesman problems
Cybernetics and Systems
A man-machine approach toward solving the traveling salesman problem
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Repair and Brood Selection in the Traveling Salesman Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Some NP-complete geometric problems
STOC '76 Proceedings of the eighth annual ACM symposium on Theory of computing
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
How an optimal observer can collapse the search space
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary transitions as a metaphor for evolutionary optimisation
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
CoEvolution of effective observers and observed multi-agents system
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Toward minimal restriction of genetic encoding and crossovers for the two-dimensional Euclidean TSP
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Symbiosis enables the evolution of rare complexes in structured environments
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
The Gestalt heuristic: emerging abstraction to improve combinatorial search
Natural Computing: an international journal
Hi-index | 0.00 |
The basic idea to defend in this paper is that an adequate perception of the search space, sacrificing most of the precision, can paradoxically accelerate the discovery of the most promising solution zones. While any search space can be observed at any scale according to the level of details, there is nothing inherent to the classical metaheuristics to naturally account for this multi-scaling. Nevertheless, the wider the search space the longer the time needed by any metaheuristic to discover and exploit the "promising" zones. Any possibility to compress this time is welcome. Abstracting the search space during the search is one such possibility. For instance, a common Ordering Genetic Algorithm (o-GA) is not well suited to efficiently resolve very large instances of the Traveling Salesman Problem (TSP). The mechanism presented here (reminiscent of Gestalt psychology) aims at abstracting the search space by substituting the variables of the problems with macro-versions of them. This substitution allows any given metaheuristic to tackle the problem at various scales or through different multi-resolution lenses. In the TSP problem to be treated here, the towns will simply be aggregated into regions and the metaheuristics will apply on this new one-level-up search space. The whole problem becomes now how to discover the most appropriate regions and to merge this discovery with the running of the o-GA at the new level.