No abstract

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the HTGA approach can be more robust, statistically sound, and quickly convergent. First, the authors evaluate the performance of the presented HTGA approach by studying some global numerical optimization problems. Then, the presented HTGA approach is effectively applied to solve three examples on forecasting the sunspot numbers, tuning the associative memory, and solving the XOR problem. The numbers of hidden nodes and the links of the feedforward neural network are chosen by increasing them from small numbers until the learning performance is good enough. As a result, a partially connected feedforward neural network can be obtained after tuning. This implies that the cost of implementation of the neural network can be reduced. In these studied problems of tuning both network structure and parameters of a feedforward neural network, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the presented HTGA approach can obtain better results than the existing method reported recently in the literature.

No abstract

No abstract

No abstract

A niche genetic algorithm (GA) based on a novel twinspace crowding (TC) approach is proposed for solving multimodal manufacturing optimization problems. The proposed TC method is designed in a parameter-free paradigm. That is, when cooperatively exploring solutions with GAs, it does not require prior knowledge related to the solution space to design additional problem-dependent parameters in the evolutionary process. This feature makes the proposed TC method suitable for assisting GAs in solving real-world engineering optimization problems involving intractable solution landscapes. A set of numerical benchmark functions is used to compare effectiveness and efficiency in the proposed TCGA, in different niche GAs, and in several evolutionary computation methods. The TCGA is then used to solve multimodal joint-space inverse problems in seriallink robots to compare its convergence performance with that of conventional methods that apply the sharing function. Finally, the TCGA is used to solve iterative collision-free design problems for linkage-bar robotic hands to demonstrate its effectiveness for generating diverse solutions during the design process.

In this paper, an improved genetic algorithm, called the hybrid Taguchi-genetic algorithm (HTGA), is proposed to solve the job-shop scheduling problem (JSP). The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to systematically select the better genes to achieve crossover, and consequently enhance the genetic algorithm. Therefore, the proposed HTGA approach possesses the merits of global exploration and robustness. The proposed HTGA approach is effectively applied to solve the famous FisherThompson benchmarks of 10 jobs to 10 machines and 20 jobs to 5 machines for the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the proposed HTGA approach can obtain both better and more robust results than other GA-based methods reported recently.

No abstract