Modern heuristic techniques for combinatorial problems
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
Evolutionary Algorithms for Vertex Cover
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
When gravity fails: local search topology
Journal of Artificial Intelligence Research
Prototype selection algorithms for distributed learning
Pattern Recognition
Cuisine: Classification using stylistic feature sets and-or name-based feature sets
Journal of the American Society for Information Science and Technology
An agent-based simulated annealing algorithm for data reduction
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Cluster integration for the cluster-based instance selection
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Distributed learning with data reduction
Transactions on computational collective intelligence IV
A new cluster-based instance selection algorithm
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
International Journal of Applied Mathematics and Computer Science - Semantic Knowledge Engineering
Agent-Based approach to RBF network training with floating centroids
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
Subset selection problems are relevant in many domains. Unfortunately, their combinatorial nature prohibits solving them optimally in most cases. Local search algorithms have been applied to subset selection with varying degrees of success. This work presents COMPSET, a general algorithm for subset selection that invokes an existing local search algorithm from a random subset and its complementary set, exchanging information between the two runs to help identify wrong moves. Preliminary results on complex SAT, Max Clique, 0/1 Multidimensional Knapsack and Vertex Cover problems show that COMPSET improves the efficient stochastic hill climbing and tabu search algorithms by up to two orders of magnitudes.