Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization

Tsai, Jinn-Tsong; Liu, Tung-Kuan; Chou, Jyh–Horng

doi:10.1109/tevc.2004.826895

Cited by 457 publications

(240 citation statements)

References 8 publications

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“…• Fast evolution programming (FEP) [Yao et al 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]…”

Section: Why Differential Evolution?mentioning

confidence: 99%

Opposition-Based Differential Evolution

Rahnamayan

Tizhoosh

Salama

Studies in Computational Intelligence

245

348

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Section: Why Differential Evolution?mentioning

confidence: 99%

Opposition-Based Differential Evolution

Rahnamayan

Tizhoosh

Salama

Studies in Computational Intelligence

245

348

View full text Add to dashboard Cite

“…Such examples are as follows: in [209], a PSO-PSO method was proposed, in which a PSO (inner PSO block) was applied for optimizing weights that were nested under another PSO (outer PSO block) which was applied for optimizing the architecture of FNN by adding or deleting hidden node. 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). A multidimensional PSO approach was proposed in [212] for constructing FNN automatically by using an architectural (topological) space.…”

Section: Architecture Plus Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

465

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…Although, a plethora of literature pertaining to the search strategies adopted by various EAs is available (Nissen and Propach, 1998;Rana et al, 1996;Kazarlis et al, 2001;Yao et al, 1999;Salomon, 1998;Choi and Oh, 2000;Yoon and Moon, 2002;Kim and Myung, 1997;Storn, 1999), most of them restrict themselves to solving a particular class of optimization problem and thus, fail to provide generalized strategies that can be robustly used for wide spectrum of optimization problems in science, business and engineering applications. In general, optimization problems can be classified into two groupsnumerical optimization and combinatorial optimization (Tsai et al, 2004;Gen and Cheng, 1999). Yet another classification exists that is based on the representation schemes-binary string, floating point string and integer bit string representation (Rana et al, 1996;Kazarlis et al, 2001;Yao et al, 1999;Salomon, 1998;Choi and Oh, 2000; Yoon and Moon, 2002;Kim and Myung, 1997;Storn, 1999;Zhang and Leung, 1999;Burke and Newall, 1999).…”

Section: Introductionmentioning

confidence: 99%

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Dashora

Kumar

Shukla³

et al. 2008

Engineering Applications of Artificial Intelligence

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This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A theoretical analysis has been performed to establish the convergence of the learning rule. A theoretical study has also been performed in order to investigate the various aspects of the search strategy employed by the new tradeoff-based mutations. A more logical parameter tuning has been done by introducing the concept of orthogonal arrays in the EA experimentation. The use of noise-based tuning ensures the robust parameter tuning that enables the EAs to perform remarkably well in the further experimentations. The performance of the proposed EAs has been analyzed for different problems of varying complexities. The results prove the supremacy of the proposed EAs over other well-established strategies given in the literature. r

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Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization

Cited by 457 publications

References 8 publications

Opposition-Based Differential Evolution

Opposition-Based Differential Evolution

Metaheuristic design of feedforward neural networks: A review of two decades of research

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

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