An orthogonal genetic algorithm with quantization for global numerical optimization

By Y. Leung, Yuping Wang. IEEE Transactions on Evolutionary Computation, 2001

840 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/693acaa8c2d21300d709baee659edf94999df6db

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In Artificial Intelligence

In Computational Theory and Mathematics

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Citations in the corpus

Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm (2006, methodology): “In the work of Leung and Wang [33], the following evolutionary environments are used: 1) the population size is 200; 2) the crossover rate is 0.”
A GA-API Solution for the Economic Dispatch of Generation in Power System Operation (2012, methodology): “Note that in Table II, OGA/Q denotes the orthogonal genetic algorithm with quantization [29]; M-L is the modified mean-level-set method [26]; LEA is the level-set evolution; and Latin squares algorithm [26].”
Novel composition test functions for numerical global optimization (2005, background): “Some co-evolutionary algorithms [3] and the one dimensional mutation operator [1][4] just use these properties to locate the global optimum rapidly.”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “Leung and Wang [11] used OED technique in GA to generate the initial population.”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, background): “…crossover operator is just a vertex of the hyper-rectangle defined by the mutant and target vectors, Wang et al. [11] employed orthogonal crossover [16] to make a systematic and rational search in the hyper-rectangle defined by the mutant and target vectors, and proposed a generic framework to…”
Recent advances in differential evolution - An updated survey (2016, methodology): “Very recently, Li et al. (2015) studied different ways of population generation by DE and CMA-ES.”
A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning (2013, methodology): “tions of dimensions D = 50, 100, or 200 [36], [38], as shown in Table V.”
Opposition based learning: A literature review (2017, background): “[58] proposed a local search to increase the performance DE which uses an orthogonal crossover [137,340].”
Hybrid Taguchi-genetic algorithm for global numerical optimization (2004, methodology): “Therefore, in recent years, some researchers [1], [3], [5], [7], [12], [14], [17], [20], [22] have proposed various improved-GAs”
A multiagent genetic algorithm for global numerical optimization (2004, methodology): “[ 8 ] compared OGA/Q with traditional GAs and five existing algorithms, and the results showed that OGA/Q outperforms all the other methods.”
Orthogonal Learning Particle Swarm Optimization (2009, methodology): “Leung and Wang [42] proposed another orthogonal GA (OGA/Q) using OED to improve the population initialization and to enhance the crossover operator.”
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example (2012, methodology): “Until now, algorithms that are commonly used to treat optimization problems and data mining include: regression and classification [18–21], genetic algorithm [14,2], Ant Colony Optimization Algorithm [6,4] and Particle Swarm Optimization Algorithm [8,25].”
An efficient multiobjective differential evolution algorithm for engineering design (2009, methodology): “As suggested in [17], to generate an OA, we use J = 2, and Q = 11 if n < 10; otherwise, Q = n − 1.”