Hybrid Taguchi-genetic algorithm for global numerical optimization
By Jinn-Tsong Tsai, Tung-Kuan Liu, Jyh-Horng Chou. IEEE Transactions on Evolutionary Computation, 2004
513 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/9798536bb2654af9f0fe668a28694ae3ea514b88
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shahryar Rahnamayan
- Magdy M. A. Salama
- Hamid R. Tizhoosh
- Václav Snásel
- Varun Ojha
- Ajith Abraham
- Jyh-Horng Chou
- Tung-Kuan Liu
In Computational Theory and Mathematics
- Shahryar Rahnamayan
- Magdy M. A. Salama
- Hamid R. Tizhoosh
- Ajith Abraham
- Václav Snásel
- Varun Ojha
- Jyh-Horng Chou
- Tung-Kuan Liu
In Management Science and Operations Research
- Magdy M. A. Salama
- Ajith Abraham
- Václav Snásel
- Shahryar Rahnamayan
- Varun Ojha
- Hamid R. Tizhoosh
- Jyh-Horng Chou
- Tung-Kuan Liu
Citations in the corpus
- A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design (2016, background): “The two-level standard orthogonal arrays most often used in practice are L4(2 3), L8(2 7), L16(2 15), and L32(2 31) [46].”
- Opposition-Based Differential Evolution (2008, methodology): “1999] • Fast evolutionary strategies (FES) [Yao and Liu 1997] • Hybrid taguchi-genetic algorithm (HTGA) [Tsai et al. 2004] • Orthogonal genetic algorithm (OGA) [Leung and Wang 2001] • Particle swarm optimization (PSO) [Angeline 1998] • Quantum evolutionary algorithm with rotation (QEA/R) [Han and Ki”
- A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning (2013, unknown): “For more details, see [32]–[36] and [39].”
- Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm (2006, methodology): “First, in this section, in order to make this paper more complete, the authors adopt the partial results of their works [28], [29] regarding the HTGA approach to show the performance of the presented HTGA approach.”
- Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “Similarly, in [210, 211], a hybrid Taguchi-genetic algorithm was proposed for optimizing the FNN architecture and weights, where authors used a genetic representation of the weights, but they select structure using constructive method (by adding hidden nodes one-by-one).”