Optimized Artificial Neural Network for Biosignals Classification Using Genetic Algorithm

Lima, Aron Alexandre Martins; Barros, Fábio; Yoshizumi, Victor Hideki; Spatti, Danilo Hernane; Dájer, María Eugenia

doi:10.1007/s40313-019-00454-1

Cited by 9 publications

(16 citation statements)

References 11 publications

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“…As can be seen from Figure 1 , the structural characteristics of multilayer feedforward networks are (1) no connections between neurons in the same layer, (2) Full connectivity between neurons in two adjacent layers, and (3) there is directionality in information transfer. The forward calculation is done layer by layer from the input to the output [ 20 ].…”

Section: Product Styling Design Evaluation Methods Based On Multilayer Perceptron Genetic Algorithm Neural Network Algorithmmentioning

confidence: 99%

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

Wu¹

2021

Computational Intelligence and Neuroscience

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Products no longer exist simply as carriers of useful functions, but more and more consumers are beginning to pay attention to the spiritual aspects of the feelings brought by products. This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural network (GA-MLP-NN) algorithm, quantifying the product shape, using computer-aided design technology to achieve shape optimization, shape, and color scheme generation, and using interactive feedback with users to finally generate a product shape with market demand. In this paper, we use the combinatorial innovation method to arrange and combine the detail elements in the solution library to generate the modeling solution, combine the multilayer perceptron genetic algorithm neural network algorithm with product modeling, and establish the interactive genetic modeling system for the product, use this system to design the product modeling solution, and finally get the product modeling solution satisfied by the target users; using the multilayer perceptron genetic algorithm neural network method to evaluate the product modeling items. The mapping relationship model between morphological feature space and imagery cognitive space was constructed based on multiple linear regression equations, and the multiple regression model for each affective dimension was ideal. The results show that the model performance is reliable. The weights are calculated, and the appropriate people are selected to score and calculate the modeling scheme, and finally, the satisfactory product modeling scheme is obtained.

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Section: Product Styling Design Evaluation Methods Based On Multilayer Perceptron Genetic Algorithm Neural Network Algorithmmentioning

confidence: 99%

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

Wu¹

2021

Computational Intelligence and Neuroscience

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“…A common solution to this challenge is to migrate the computation of these functions to parallel machines [10,11]. Therefore, regarding the wide use of GA in a variety of problems such as optimization [12], image processing [13] artificial neural networks training [14] and rulebased systems [15] many researchers try to investigate parallelization methods for GAs on multi-core systems as well as many-core systems.…”

Section: Introductionmentioning

confidence: 99%

An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems

et al. 2020

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A novel parallelization method of genetic algorithm (GA) solution of the Traveling Salesman Problem (TSP) is presented. The proposed method can considerably accelerate the solution of the equivalent TSP of many complex vehicle routing problems (VRPs) in the cloud implementation of intelligent transportation systems. The solution provides routing information besides all the services required by the autonomous vehicles in vehicular clouds. GA is considered as an important class of evolutionary algorithms that can solve optimization problems in growing intelligent transport systems. But, to meet time criteria in time-constrained problems of intelligent transportation systems like routing and controlling the autonomous vehicles, a highly parallelizable GA is needed. The proposed method parallelizes the GA by designing three concurrent kernels, each of which running some dependent effective operators of GA. It can be straightforwardly adapted to run on many-core and multi-core processors. To best use the valuable resources of such processors in parallel execution of the GA, threads that run any of the triple kernels are synchronized by a low-cost switching mechanism. The proposed method was experimented for parallelizing a GA-based solution of TSP over multi-core and many-core systems. The results confirm the efficiency of the proposed method for parallelizing GAs on many-core as well as on multi-core systems.

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“…Meta-heuristic optimization algorithms [1][2][3] like Genetic Algorithms (GAs) have been widely used in science and engineering problems [3][4][5]. GA is considered as a class of evolutionary algorithms that are used for finding approximate solutions in search [6], optimization problems [7,8], image processing [9], optimizing artificial neural networks [10], scheduling [11,12], and rule-based systems [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Parallelization of a Genetic Algorithm Solution on the Traveling Salesman Problem with Multi-core and Many-core Systems

Abbasi

Rafiee

2020

IJE

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Efficient parallelization of genetic algorithms (GAs) on state-of-the-art multi-threading or manythreading platforms is a challenge due to the difficulty of scheduling hardware resources regarding the concurrency of threads. In this paper, for resolving the problem, a novel method is proposed, which parallelizes the GA by designing three concurrent kernels, each of which are running some dependent effective operators of GA. The proposed method can be straightforwardly adapted to run on many-core and multi-core processors by using Compute Unified Device Architecture (CUDA) and Threading Building Blocks (TBB) platforms. To efficiently use the valuable resources of such computing cores in concurrent execution of the GA, threads that run any of the triple kernels are synchronized by a considerably fast switching technique. The offered method was used for parallelizing a GA-based solution of Traveling Salesman Problem (TSP) over CUDA and TBB platforms with identical settings. The results confirm the superiority of the proposed method to state-of-the-art methods in effective parallelization of GAs on Graphics Processing Units (GPUs) as well as on multi-core Central Processing Units (CPUs). Also, for GA problems with a modest initial population, though the switching time among GPU kernels is negligible, the TBB-based parallel GA exploits the resources more efficiently.

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Optimized Artificial Neural Network for Biosignals Classification Using Genetic Algorithm

Cited by 9 publications

References 11 publications

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems

Efficient Parallelization of a Genetic Algorithm Solution on the Traveling Salesman Problem with Multi-core and Many-core Systems

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