DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Digital IIR filter design using multi-objective optimization evolutionary algorithm
Applied Soft Computing
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
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
Hi-index | 0.00 |
In many challenging numerical optimization problems, the conflict between exploitation and exploration abilities of EAs must be balanced in an effective and efficient way. In the previous research, in order to address this issue, the Two-Stage ensemble Evolutionary Algorithm(TSEA)was originally proposed for engineering application. In TSEA, the optimization is divided into two relatively separate stages, which aims at handling the exploitation and exploration in a more reasonable way. In this paper, we try to extend the application area of TSEA from specific engineering problems to general numerical optimization problems by altering its sub-optimizers. The experimental studies presented in this paper contain three aspects: (1) The benefits of the TSEA framework are experimentally investigated by comparing TSEA with its sub-optimizers on 26 test functions; then (2) TSEA is compared with diverse state-of-the-art evolutionary algorithms (EAs) to comprehensively show its advantages; (3) To benchmark the performance of TSEA further, we compare it with 4 classical memetic algorithms (MAs) on CEC05 test functions. The experimental results definitely demonstrate the excellent effectiveness, efficiency and reliability of TSEA.