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Many existing software reliability models are based on some subjective assumptions those could be easily impractical in reality. Genetic Programming(GP for short) does not need some subjective assumption due to the basic characteristic of the data. Also, this method doesn't require to understand the inherent processes for failures, but to create models based on the given data for a "true"...
The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic...
Firstly, in this paper we propose an improved immune algorithm, that is, introduce the Metropolis criterion into the selection operation of immune algorithm, and the Metropolis immune algorithm (MIA) is formed, then we carry out the theoretical analysis and experimental simulation aiming at the performance of the MIA; secondly, we use this algorithm to excavate association rules, and propose a new...
This paper combines quantum evolutionary algorithm with the available evolutionary algorithms, uses quantum computing parallelism to search solution space efficiently, uses the traditional evolutionary algorithm to optimize the result observed by quantum population and then uses the optimized result to guide the generation of some individuals of quantum population. It maintains the population diversity...
To solve the problems of the incongruence of software reliability models and cast off the traditional models' multi-subjective assumptions, this paper adopts genetic programming evolution algorithm which has adaptive genetic operators (for short AGP) to establish software reliability model based on software failure time series. The individual of the population is according to the case of the fitness...
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