Test Set Generation for Pairwise Testing Using Genetic Algorithms

2015 ◽  
Keyword(s):  
Genetic Algorithms ◽  
Test Set ◽  
Pairwise Testing

An Approach to Test Set Generation for Pair-Wise Testing Using Genetic Algorithms

2013 ◽  
pp. 294-299 ◽  
Author(s):  
Priti Bansal ◽  
Sangeeta Sabharwal ◽  
Shreya Malik ◽  
Vikhyat Arora ◽  
Vineet Kumar
Keyword(s):  
Genetic Algorithms ◽  
Test Set

scholarly journals GAPSO: Optimal Test Set Generator for Pairwise Testing

2019 ◽  
Vol 8 (6) ◽  
pp. 2346-2350
Keyword(s):  
Software Systems ◽  
Test Cases ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Set ◽  
New Approach ◽  
Time Consumption ◽  
Pairwise Testing ◽  
The Cost ◽  
High Degree

Exhaustive testing is impossible for all sorts of software systems, owing to the cost and time consumption. Combinatorial testing is the solution to this issue and aims at picking the necessary set of parameters which can ensure high degree of interaction between the parameters. This paper presents a new approach for generating unique test cases by exploiting Genetic and Particle Swarm Optimization (GAPSO) algorithm for achieving pairwise testing. The generated test cases are refined, so as to arrive at the optimal test set. The outcome of the proposed algorithm is the minimal count of high quality test cases.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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