Binary versus real coding for genetic algorithms: A false dichotomy?

Gaffney, Janice; Pearce, Charles E. M.

doi:10.21914/anziamj.v51i0.2776

Cited by 19 publications

(13 citation statements)

References 4 publications

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“…Therefore, the integer test inputs are converted in binary using (8× number of inputs) bits for reproduction (8 bits are sufficient to represent each test input in the range [−10, 110]). There are variants for chromosomes encoding in GA e.g., gray, binary and, real, and each has its own advantages and disadvantages [68][69][70]. Binary encoding is beneficial to include a sudden change in the population of solutions, which is desirable in the current study to diversify the population for increasing the chances of detecting live mutants.…”

Section: Description Of Proposed Approachmentioning

confidence: 99%

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

2019

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Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

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Section: Description Of Proposed Approachmentioning

confidence: 99%

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

2019

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“…The differences are found in the implementation of the operators, through significantly different algorithms, which has an important effect on the results (Gaffney et al 2010). Here, operators are defined as the mechanisms that modify the values of the genes, to try to bring individuals (or chromosomes) closer to an optimum of the fitness function.…”

Section: A Structure and Operatorsmentioning

confidence: 99%

Global Optimization of an Analog Method by Means of Genetic Algorithms

Horton

Jaboyedoff

Obled

2017

Monthly Weather Review

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Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

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“…According to [16], the binary code converges faster when M rate > 0.6. Thus, the binary coding was selected to encode the discrete controller parameters into binary string to generate the initial population randomly in the beginning.…”

Section: Discrete Pid Parameters Optimisation By Sgasmentioning

confidence: 99%