Cluster-based Adaptive Mutation Mechanism To Improve the Performance of Genetic Algorithm

Sun, Tsung-Ying; Liu, Chan-Cheng; Hsieh, Sheng-Ta; Lin, Chun‐Ling; Lee, Kan-Yuan

doi:10.1109/isda.2006.123

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…In other words, individuals of GA rapidly move toward the currently best solution and ignore other feasible space. Based on selection operation of survive the fittest, premature convergence could not be improved efficiently even if mutation operation is carried out [12]. Individual movement of PSO does not only refer to the currently best solution but involves with self best solution and past movement, as (5).…”

Section: Discussionmentioning

confidence: 99%

“…However, the fitness value of (10) will not correctly reflect situation of separation while scales of y i do not satisfy σ 2 (y) = 1. The unit variance process proposed by [3] is involved with each generation of PSO, it consists of two steps: in the first step, the normalized recover signals are calculated by: (12) then the normalized particles are calculated in the second step by:…”

Section: Fitness Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network

Liu

Sun

Hsieh

et al. 2006

Neural Information Processing

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Abstract. The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system.

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Section: Discussionmentioning

confidence: 99%

Section: Fitness Evaluationmentioning

confidence: 99%

A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network

Liu

Sun

Hsieh

et al. 2006

Neural Information Processing

Self Cite

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“…The performance of the MEGA is compared with those of four well-known GAs: cluster-based adaptive mutation genetic algorithm (CMGA) [18], orthogonal genetic algorithm with quantization (OGA/Q) [19], hybrid taguchi genetic algorithm (HTGA) [20], and StGA [7]. The OGA/Q is a quantizedversion of the OGA, which incorporates with the Taguchi method to minimize the effect of chromosome variation without eliminating the population diversity [21].…”

Section: Comparison With Other Genetic Algorithmsmentioning

confidence: 99%

A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

Lin

2013

Journal of Applied Mathematics

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Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems.

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Optimal determination of wavelet threshold and decomposition level via heuristic learning for noise reduction

Sun

Liu

Hsieh

et al. 2008

2008 IEEE Conference on Soft Computing in Industrial Applications

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Cluster-based Adaptive Mutation Mechanism To Improve the Performance of Genetic Algorithm

Cited by 4 publications

References 22 publications

A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network

A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network

A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

Optimal determination of wavelet threshold and decomposition level via heuristic learning for noise reduction

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