Exploring dynamic self-adaptive populations in differential evolution

By Jason Teo. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2006

382 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/bb06c6c9c8fa02ff8b424b57ce7c742a18c3c0ac

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Citations in the corpus

Population size reduction for the differential evolution algorithm (2008, methodology): “Although the DE algorithm has been shown to be a simple, yet powerful, evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process [28].”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, methodology): “Besides the adaptation of the control parameters F and Cr, Teo [17] investigated the population sizing problem via self-adaptation and proposed two different approaches, one adopts an absolute encoding strategy for NP, and the other adopts a relative encoding strategy for NP.”
Opposition-Based Differential Evolution (2008, methodology): “Teo proposed a dynamic population sizing strategy based on self-adaptation [17] and Ali and Törn [18] introduced auxiliary population and automatic calculating of the amplification factor , for the difference vector.”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Teo [102] proposed DE with self-adaptive population size NP (abbreviated as DESAP), based on self-adaptive Pareto DE proposed by Abbas [1].”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, methodology): “Besides adapting the control parameters or , Teo proposed differential evolution with self adapting populations (DESAP) [22], based on Abbass's self-adaptive Pareto DE. Recently, Brest et al. [28] encoded control parameters and into the individuals and adjusted by introducing two new parameters and ”
JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “Similar to many other adaptive DE algorithms (except DESAP [10]), the population size is assumed to be tuned in advance and kept ﬁxed throughout the evolution process of FADE.”
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “They concluded that those variants outperform the original DE. Teo [25] presented a dynamic self-adaptive populations DE, where the population size is self-adapting.”
Recent advances in differential evolution: a survey and experimental analysis (2010, background): “Regarding adaptive population size, a first attempt has been proposed in Teo (2005, 2006).”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, methodology): “Teo [190] derived the population size by averaging”