A genetic algorithm based on extended sequence and topology encoding for the multicast protocol in two-tiered WSN

The Genetic Algorithms In engineering structure optimization design includes Truss Structure optimization, Shape and topology optimization, Composite materials optimization, layout optimization, Multi-Objective Optimization. This paper combined with engineering background , Selecting the Pressing gear of CNC crushing Machine Steady as starting point for Optimization modeling .Analyzing the simple conditions theoretical physical model of CNC Crushing Machine Steady, Reasonably selected design variables ,using Conventional Methods and genetic algorithms to optimize the Steady ,obtainning every iterative step relevant data under the two methods, and analyzing the results ,analysis the accuracy of the optimize results through the stress and displacement map .

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Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization

Journal of Spectroscopy ◽

10.1155/2016/5616503 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12

Author(s):

Qian Wang ◽

Boyan Cai ◽

Yajie Yu ◽

Hui Cao

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Quantitative Analysis ◽

Structure Optimization ◽

Wavelength Selection ◽

Analysis Model ◽

Backpropagation Neural Network ◽

Topology Structure ◽

Full Spectrum ◽

And Topology

Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP). The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool.

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Logic Rules for Automated Synthesis of Function Models Using Evolutionary Algorithms

10.1115/detc2021-70575 ◽

2021 ◽

Author(s):

Amaninder Singh Gil ◽

Chiradeep Sen

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Automated Synthesis ◽

Function Model ◽

Rule Based ◽

Model Synthesis ◽

And Topology ◽

Software Code ◽

Rule Based Approach ◽

Function Models

Abstract This paper presents the development of logic rules for evaluating the fitness of function models synthesized by an evolutionary algorithm. A set of 65 rules for twelve different function verbs are developed. The rules are abstractions of the definitions of the verbs in their original vocabularies and are stated as constraints on the quantity, type, and topology of flows connected to the functions. The rules serve as an objective and unambiguous basis of evaluating the fitness of function models developed by a genetic algorithm. The said algorithm and the rules are implemented in software code, which is used to both demonstrate and validate the efficacy of the rule-based approach of converging function model synthesis using GAs.

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