Automatic Generation of Design Scheme Based on Improved Segmentation Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1655-1660
Author(s):  
Ji Wen Chen ◽  
Bo Huang ◽  
Bo Pang ◽  
Li Li
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Information Seeking ◽  
Structural Information ◽  
Fitness Function ◽  
Automatic Generation ◽  
Genetic Operators ◽  
Structure Information ◽  
Design Scheme

Research on composition and quantitative representation of the function unit structural information, seeking the combination process effectively, is the key technology to generate product design schemes. Full life cycle assessment properties of structure is introduced into expression of design scheme. The gene model of life cycle assessment properties of structure is established, and the variable length coding is converted to equal length coding to realize the quantitative representation of structure information. The fitness function is established for life cycle assessment of structure with Analytic Hierarchy Process. The improved segmentation genetic algorithm is studied. The gene sequence of design scheme is segmented. Genetic operators such as across and mutate is designed for structure information the segmented gene fragment. Life cycle assessment gene of structure as attribute does not participate in the genetic operation. Design schemes automatic generation is achieved based on improved segmented genetic operators, reflecting life cycle assessment properties of design schemes.

Scheme Automatic Generation Based on Full Life Cycle Assessment Gene Model of Structure

2014 ◽  
Vol 687-691 ◽  
pp. 1622-1627
Author(s):  
Ji Wen Chen ◽  
Hong Juan Yang ◽  
Nan Xu ◽  
Li Li
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Fitness Function ◽  
Automatic Generation ◽  
Genetic Operators ◽  
Gene Model ◽  
Structure Information ◽  
Full Life Cycle ◽  
Diamond Wire Saw ◽  
Full Life

Considering the inheritance and hereditary of product structure life cycle assessment, full life cycle assessment properties of structure is introduced into expression of design scheme. Scheme evolution design is presented based on gene model of full life cycle assessment properties of structure. The gene model of life cycle assessment properties of structure is established. The variable length coding is converted to equal length coding to realize the quantitative representation of structure information. The fitness function is established for life cycle assessment of structure by Analytic Hierarchy Process. The genetic operators are designed. Scheme evolution design is realized based on gene model of life cycle assessment of structure, reflecting life cycle assessment properties of design schemes, improving the efficiency of product design generation. The evolution design example of multi-rope diamond wire saw verifies the feasibility of the imposed method.

A Multiple Objective Fitness Function Design Scheme for GA-Based Cognitive Engines

2015 ◽  
Vol 764-765 ◽  
pp. 444-447
Author(s):  
Keun Hong Chae ◽  
Hua Ping Liu ◽  
Seok Ho Yoon
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Fitness Function ◽  
Multiple Objective ◽  
Cognitive Engine ◽  
Fitness Functions ◽  
Design Scheme ◽  
Transmission Parameter ◽  
Function Design ◽  
Single Objective

In this paper, we propose a multiple objective fitness function for cognitive engines by using the genetic algorithm (GA). Specifically, we propose four single objective fitness functions, and finally, we propose a multiple objective fitness function based on the single objective fitness functions for transmission parameter optimization. Numerical results demonstrate that we can obtain transmission parameter sets optimized for given transmission scenarios with the GA-based cognitive engine incorporating the proposed objective fitness function.

scholarly journals Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

2017 ◽  
Vol 44 (11) ◽  
pp. 945-955 ◽  
Author(s):  
Mansour Fakhri ◽  
Ershad Amoosoltani ◽  
Mona Farhani ◽  
Amin Ahmadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
Crumb Rubber ◽  
Concrete Pavement ◽  
Genetic Operators ◽  
Efficiency Criterion ◽  
Roller Compacted Concrete

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

Hardware Implementation of a Genetic Algorithm for Motion Path Planning

2016 ◽  
pp. 250-275
Author(s):  
Imbaby I. Mahmoud ◽  
May Salama ◽  
Asmaa Abd El Tawab Abd El Hamid
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Fitness Function ◽  
Random Number Generation ◽  
Motion Path ◽  
Genetic Operators ◽  
Fitness Evaluation ◽  
Evaluation Task ◽  
Evaluation Module ◽  
The Individual

The aim of this chapter is to investigate the hardware (H/W) implementation of Genetic Algorithm (GA) based motion path planning of robot. The potential benefit of using H/W implementation of genetic algorithm is that it allows the use of huge parallelism which is suited to random number generation, crossover, mutation and fitness evaluation. The operation of selection and reproduction are basically problem independent and involve basic string manipulation tasks. The fitness evaluation task, which is problem dependent, however proves a major difficulty in H/W implementation. Another difficulty comes from that designs can only be used for the individual problem their fitness function represents. Therefore, in this work the genetic operators are implemented in H/W, while the fitness evaluation module is implemented in software (S/W). This allows a mixed hardware/software approach to address both generality and acceleration. Moreover, a simple H/W implementation for fitness evaluation of robot motion path planning problem is discussed.

scholarly journals A Novel Fast Decision-making Method Based on Integrated Intelligent Technologies for Wind Disaster Management of Energy Manufacture

CONVERTER ◽  
2021 ◽  
pp. 169-190
Author(s):  
Baishang Zhang, Et al.
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Evidence Theory ◽  
Nonlinear Problems ◽  
Case Based Reasoning ◽  
Genetic Operators ◽  
Electricity Grid ◽  
Fast Decision

Energy manufacture is very important to all of industries. Typhoons hit the power grid in China's southeast coastal areas frequently for the past few years, seriously affecting the industries’ operation. Therefore, making-decision of wind damage management for nation's electricity grid in real time is an urgent subject to be studied. The traditional decision making method is easy to be implemented, but is not proper for dealing with nonlinear problems in complex systems. The purpose of this article is to design a fast decision making framework for accomplishing fast decision making by making combination Case-Based Reasoning (CBR) with Rule-Based Reasoning (RBR), Genetic Algorithm (GA), which is called fast decision making method based on integrated intelligent technologies (FDMMBIIT). Compared with traditional methods, FDMMBIIT completes case adaptation with BPNN after extending case base. To make the decision-making more accurate, this article updated the multi-object genetic algorithm (MOGA) with adaptive genetic operators and a selection method by using the fitness function. Likewise, BPNN is improved with adaptive simulated annealing algorithm (ASAA), which is named as BPNNASAA. More important, this paper expands the frame theory by integrating it to the D/S evidence theory, exploring a novel solution to representing cases with incomplete information. The case of Guangdong demonstrates FDMMBIIT achieves better decision-making performance for storm disaster emergency management.

Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 961-966
Author(s):  
Fei Xiang ◽  
Shan Li
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Adaptive Mutation ◽  
System Response ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Adaptive Immune ◽  
Design Scheme

For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.

Improved Genetic Algorithms for Software Testing Cases Generation

2013 ◽  
Vol 380-384 ◽  
pp. 1464-1468
Author(s):  
Shun Kun Yang ◽  
Fu Ping Zeng
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Fitness Function ◽  
Automatic Generation ◽  
Improved Genetic Algorithm ◽  
Dynamic Adjustment ◽  
Genetic Operators ◽  
Adaptive Genetic Algorithms ◽  
Crossover And Mutation ◽  
Testing Coverage

In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and realize the biggest testing coverage to find more defects as far as possible. Finally, the improved Genetic Algorithm are compared and analyzed by testing one benchmark program to verify its feasibility and effectiveness.

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