Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

By Jun Zhang, Henry Shu-Hung Chung, Wai Lun Lo. IEEE Transactions on Evolutionary Computation, 2007

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An Overview of Artificial Intelligence Applications for Power Electronics (2020, unknown): “Clustering k-means – Simple implementation – Sensitive to outliers Control [129], Maintenance [118], [130]–[132]”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, methodology): “1) applying other algorithms like GA [59], [60], ant colony optimization [61]–[63], DE [64]–[67], EDA, MA [68]–[72], and their improved variants (like enhanced PSO variants [73]–[76])”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…impossible or extremely difficult by using the classical optimization techniques (Karaboga and Akay 2009a; Nowak and Cirpka 2004; Clerc and Kennedy 2002; Trelea 2003; Dorigo et al. 1996; Ong et al. 2006; Storn and Price 1997; Yang and Deb 2010; Das et al. 2011; Yang 2005, 2009; Zhang et al. 2007).”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “Zhang et al. [ 58 ] proposed the use of CA to divide the chromosomes of GA into several groups based on their position information.”
Adaptive Particle Swarm Optimization (2008, methodology): “The notion of evolutionary states was first introduced in [50] and [51], where a clustering analysis technique was used to determine the states.”