Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic algorithms for dynamic test data generation
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
Breeding Software Test Cases with Genetic Algorithms
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Investigating the performance of genetic algorithm-based software test case generation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
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This paper presents the study of optimization of software testing techniques by using Genetic Algorithms (GAs) and a sufficient testing convergence condition of GAs is presented. Some new categories of genetic codes are applied in some problem optimizations for the generation of reliable software test cases. These GAs have found their application in detecting errors in the software packages. For example, based on Symmetric Codes theory, new genetic strategy, GA with symmetric code is developed. In the current paper, some key definitions of genetic transformation have been used viz. crossover, mutation and selection. Some of our research shows that genetic encoding techniques have very important influence on the performance of software test cases. This paper is organized into three parts: part I describes the functionality of GAs, part II presents the usage of GAs in software testing to the alternatives of existing software testing techniques, part III discusses the implementation of GAs using MATLAB for the generation of optimized test cases.