Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

2002 ◽  
Vol 2 (2) ◽  
pp. 106-114 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan S. Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms are used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for an automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are described. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

Estimating Target Values in Preliminary Design: An Automotive Case Study

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Fuel Economy ◽  
Preliminary Design ◽  
House Of Quality ◽  
Acceleration Time ◽  
Engineering Characteristics ◽  
Target Values ◽  
Percent Difference ◽  
Average Percent Difference

Abstract Estimation modules have been developed for use with the House of Quality. These estimation modules are used to predict the performance of a proposed design based on the values of the Engineering Characteristics. This, paper describes the development of modules for an automotive case study. Specifically, modules for weight, price, acceleration time, and fuel economy are given. Comparison of estimated values to actual values show an average percent difference of less than 10%.

A Strategy for Extending the House of Quality to Obtain Preliminary Design Specifications

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithms ◽  
Automotive Industry ◽  
Test Case ◽  
Preliminary Design ◽  
House Of Quality ◽  
Design Specifications

Abstract This paper details a strategy for quantifying the House of Quality to produce preliminary design specifications. Estimation modules replace the traditional symbols in the HoQ. Genetic algorithms are used to produce and rate candidate preliminary design specifications. A test case from the automotive industry is implemented in order to test the effectiveness of the strategy. Results from the test case indicate that the strategy produces preliminary design specifications that are reasonable and consistent.

scholarly journals Lecture Scheduling Automation Using Genetic Algorithm

2019 ◽  
Vol 8 (5C) ◽  
pp. 1444-1447
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Results ◽  
Scheduling Problems ◽  
Scheduling System ◽  
Parameter Values

This paper aims produce an academic scheduling system using Genetic Algorithm (GA) to solve the academic schedule. Factors to consider in academic scheduling are the lecture to be held, the available room, the lecturers and the time of the lecturer, the suitability of the credits with the time of the lecture, and perhaps also the time of Friday prayers, and so forth. Genetic Algorithms can provide the best solution for some solutions in dealing with scheduling problems. Based on the test results, the resulting system can automate the scheduling of lectures properly. Determination of parameter values in Genetic Algorithm also gives effect in producing the solution of lecture schedule

Optimization of Multi-Modal Discrete Functions Using Genetic Algorithms

1993 ◽  
Vol 207 (1) ◽  
pp. 53-59 ◽  
Author(s):  
D T Pham ◽  
Y Yang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Approximate Solutions ◽  
Solution Set ◽  
Preliminary Design ◽  
Potential Solution ◽  
Reproduction Cycle ◽  
Fitness Sharing ◽  
Discrete Functions

Four techniques are described which can help a genetic algorithm to locate multiple approximate solutions to a multi-modal optimization problem. These techniques are: fitness sharing, ‘eliminating’ identical solutions, ‘removing’ acceptable solutions from the reproduction cycle and applying heuristics to improve sub-standard solutions. Essentially, all of these techniques operate by encouraging genetic variety in the potential solution set. The preliminary design of a gearbox is presented as an example to illustrate the effectiveness of the proposed techniques.

scholarly journals Tsunami Evacuation Routes Optimization using Genetic Algorithms: A Case Study in Palu

2020 ◽  
Vol 331 ◽  
pp. 01008
Author(s):  
Yusuf Anshori ◽  
Dwi Shinta Angreni ◽  
Suci Ramadhani Arifin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Tsunami Disaster ◽  
Evacuation Routes ◽  
Evacuation Route

Palu area and its surroundings, besides being very prone to earthquakes, are also prone to tsunamis. A devastating earthquake occurred On September 28, 2018, followed by a destructive and deadly tsunami that struck Palu Bay. This makes the need for proper planning in overcoming the tsunami disaster. One of them is by showing the evacuation route for people in tsunami-prone areas. This study aims to show the best route to the safe point of the tsunami using Genetic Algorithm. The results of the studies show that the best route for tsunami evacuations can be provided best depend on the available of the safe points. Some clusters, namely 9, 10, and 12 have few safe points, limiting people to access a safe location from the tsunami.

scholarly journals Optimal sampling in a noisy genetic algorithm for risk-based remediation design

2003 ◽  
Vol 5 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Gayathri Gopalakrishnan ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Fitness Function ◽  
Sampling Strategies ◽  
Robust Designs ◽  
Highly Efficient ◽  
Efficient Sampling

A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.

SIMULATION OF GENETIC ALGORITHMS ON MIMD MULTICOMPUTERS

1992 ◽  
Vol 02 (04) ◽  
pp. 381-389 ◽  
Author(s):  
I. DE FALCO ◽  
R. DEL BALIO ◽  
E. TARANTINO ◽  
R. VACCARO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Diffusion Processes ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Parallel Genetic Algorithm ◽  
Genetic Operators ◽  
Parallel Diffusion ◽  
Fully Connected ◽  
Chordal Ring

In this paper, a Parallel Genetic Algorithm has been developed and mapped onto a coarse grain MIMD multicomputer whose processors have been configured in a fully connected chordal ring topology. In this way, parallel diffusion processes of good local information among processors have been carried out. The Parallel Genetic Algorithm has been applied, specifically, to the Travelling Salesman Problem. Many experiments have been performed with different combinations of genetic operators; the test results suggest that PMX crossover can be avoided by using only the inversion genetic operator and that a diffusion process leads to improved search in Parallel Genetic Algorithms.

Optimisation of a Robotic Workcell Layout Using Genetic Algorithms

2005 ◽  
Author(s):  
Siang-Kok Sim ◽  
Meng-Leong Tay ◽  
Ahmad Khairyanto
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cycle Time ◽  
Production Cycle ◽  
Robot Arm ◽  
Layout Problem ◽  
Common Features ◽  
Placement Method ◽  
The Common

With the advent of robots in modern-day manufacturing workcells, optimization of robotic workcell layout (RWL) is crucial in ensuring the minimization of the production cycle time. Although RWL share many aspects with the well-known facility layout problem (FLP), there are features which set the RWL apart. However, the common features which they share enable approaches in FLP to be ported over to RWL. One heuristic gaining popularity is genetic algorithm (GA). In this paper, we present a GA approach to optimizing RWL by using the distance covered by the robot arm as a means of gauging the degree of optimization. The approach is constructive: the different stations within the workcell are placed one by one in the development of the layout. The placement method adopted is based on the spiral placement method first broached by Islier (1998). The algorithm was implemented in Visual C++ and a case study assessed its performance.

scholarly journals Optimisation of Rice Fertiliser Composition using Genetic Algorithms

2019 ◽  
Vol 2 (2) ◽  
pp. 72
Author(s):  
Retno Dewi Anissa ◽  
Wayan Firdaus Mahmudy ◽  
Agus Wahyu Widodo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Test Results ◽  
Random Mutation ◽  
Rice Quality ◽  
Food Scarcity ◽  
Fitness Value ◽  
Reproduction Process ◽  
Tournament Selection

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate of 0.9 and the mutation rate of 0.1. The amount of generation is 10 with the best fitness value is generated is equal to 1,603.

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