Simulated annealing: theory and applications
Simulated annealing: theory and applications
Swarm intelligence
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
A-Teams: An Agent Architecture for Optimization and Decision Support
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Ant Colony Optimization
A Development Framework for Rapid Meta-Heuristics Hybridization
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
OptLets: a generic framework for solving arbitrary optimization problems
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
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Evolutionary optimization is a well-known paradigm for solving large-scale combinatorial optimization problems. Evolutionary algorithms typically consider the fitness of solutions to decide which solution should be processed by an operator. In the presence of multiple operators to choose from, similar strategies are needed to choose an appropriate operator. In this paper, we present an adaptive target-oriented approach for evaluating and selecting operators on the fly. This technique has been integrated into the OptLets framework** [1], which monitors the success of operators and uses the results of this evaluation for operator selection in the future. Although this paper describes the technique and illustrates the results in the context of the OptLets framework, the evaluation strategy is applicable for other population-based optimization systems as well.