Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
A coevolutionary memetic particle swarm optimizer
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A learning-to-rank algorithm for constructing defect prediction models
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Journal of Parallel and Distributed Computing
A preference multi-objective optimization based on adaptive rank clone and differential evolution
Natural Computing: an international journal
A new hybrid differential evolution with simulated annealing and self-adaptive immune operation
Computers & Mathematics with Applications
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Differential evolution (DE) has become a very powerful tool for global continuous optimization problems. Parameter adaptations are the most commonly used techniques to improve its performance. The adoption of these techniques has assisted the success of many adaptive DE variants. However, most studies on these adaptive DEs are limited to some small-scale problems, e.g. with less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. The scalability performance of adaptive DE is still unclear. In this paper, based on the analyses of similarities and drawbacks of existing parameter adaptation schemes in DE, we propose a generalized parameter adaptation scheme. Applying the scheme to DE results in a new generalized adaptive DE (GaDE) algorithm. The scalability performance of GaDE is evaluated on 19 benchmark functions with problem scale from 50 to 1,000 decision variables. Based on the comparison with three other algorithms, GaDE is very competitive in both the performance and scalability aspects.