Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Implementing pure adaptive search for global optimization using Markov chain sampling
Journal of Global Optimization
Fully Automatic Test Program Generation for Microprocessor Cores
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Generating functions and the performance of backtracking adaptive search
Journal of Global Optimization
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Genetic evolutionary algorithms are effective and optimal test generation methods. However, the methods to select the algorithm parameters are often ad hoc, relying on empirical data. We used a Markov-based method to model the genetic evolutionary test generation process, parameterise the process characteristics, and derive analytical solutions for selecting the optimisation parameters. The method eliminates preliminary test generation calibration and experimentation effort needed to select these parameters, which are used in current practice.