An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computer Graphics for Engineers
Computer Graphics for Engineers
Genetic Algorithm Optimisation of Part Placement Using a Connection-Based Coding Method
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
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Applications of shearography in industry include the detection of strain anomalies which result when engineering components containing defects are subjected to stress. The output derived from shearographic apparatus is a fringe pattern which is used to confirm the integrity of, or characterise defects within, the component under test. A step towards the automation of the process is to convert the fringe lines into a mathematical representation that a computer can use for analysis. Modelling can be achieved by fitting B-spline curves to the fringe patterns and using a search to find a best fit. The paper compares the results of the run time performance of three search methods applied to this problem namely; discrete hill-climbing, random mutation hill-climbing and genetic algorithm.