Optimization of test engineering utilizing evolutionary computation

Abstract

Test engineering often experiences pressures to produce test stations and software in a short time frame with constrained budgets. Since test is a negative influence towards product costs, it is crucial to optimize the processes of test station software creation as well as the configuration of the test station itself. This paper introduces novel methodologies for optimized station configuration and automated station software generation. These two optimizations utilize evolutionary computation to automatically generate software for the test station and to offer optimal configurations of the station based upon testing requirements. Presented is a modified genetic programming algorithm for the creation of test station software (e.g. COTS software drivers). The genetic algorithm is improved through use of adaptive memory to recall historic schemas of high fitness. From the automated software generation an optimal station configuration is produced based upon the requirements of the testing to be performed. This system has been implemented in industry and an actual industrial case study is presented to illustrate the efficiency of this novel optimization technique. Comparisons with standard genetic programming techniques are offered to further illustrate the efficiency of this methodology.