Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
VLSI cell placement techniques
ACM Computing Surveys (CSUR)
Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
Adaptive operator probabilities in a genetic algorithm that applies three operators
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Proceedings of the 6th International Conference on Genetic Algorithms
Cost Based Operator Rate Adaption: An Investigation
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Pricing the 'free lunch' of meta-evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Investigations in meta-GAs: panaceas or pipe dreams?
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Issues in Real-Time Fleet Management
Transportation Science
Adapting operator settings in genetic algorithms
Evolutionary Computation
Extreme Value Based Adaptive Operator Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning and Intelligent Optimization
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
An empirical study on the synergy of multiple crossover operators
IEEE Transactions on Evolutionary Computation
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.01 |
The quality of the convergence process in genetic algorithms depends on the specific choice of strategies and combinations of operators. In this paper, we address this problem and introduce an adaptive evolutionary approach that uses a genetic algorithm in an adaptive process. An application of this approach to the dynamic vehicle routing problem with time windows is presented. We compare the adaptive version of a hybrid genetic algorithm with the non-adaptive one with respect to the robustness and the quality of the generated solutions. The results obtained show the ability of our operator combination adaptation approach to produce solutions that are superior to hand-tuning and other adaptive methods with respect to performance sensitivity and robustness.