Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a "restart with knowledge transfer" strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions.