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Cartesian Genetic Programming - Springer Nature

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Last Updated: 16 January 2022

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An Efficient MRI Impulse Noise Multi-stage Hybrid Filter Based on Cartesian Genetic Programming

However, with the one-dimensional tests to identify pixels' behavior, the typical linear filter is unable to determine noise distribution accurately due to the highly nonlinear characteristic of the impulse noise. An adaptive median filter and edge-preserving filter will be used in the recovery stage, adding three-layer filters to enhance the image and reducing the common overdependence on the result of the detection phase's failure. The filter can remove noise and maintain the structure's integrity while still shielding the structure's integrity. According to different experimental results, the recovery result under different impulse noise levels is higher than previous methods.

Source link: https://doi.org/10.1007/978-3-030-89698-0_11


Enhancing Cartesian genetic programming through preferential selection of larger solutions

We discuss how the effectiveness of Cartesian genetic programming techniques can be enhanced by the preferential selection of phenotypically larger solutions among equally good options. The one-fifth success rule applies to self-adapting the mutation rate. Eventually, we find that, for issues like the Paige regression, in which neutrality plays a smaller role, the results can be further enhanced by selectively selecting larger options among candidates with similar characteristics.

Source link: https://doi.org/10.1007/s12065-020-00421-9


Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Cartesian genetic engineering is the most cost-effective method for circuit development. Considering the multiplier design issue, for example, the 5 MATH_EQ 5-bit multiplier represents the most complicated circuit ever developed by the evolution from scratch. CGP's effectiveness is highly dependent on the work of the point mutation operator, although this operator is strictly stochastic. This contrasts with the latest advances in genetic programming, where sophisticated intelligent technologies such as semantic-aware operators are being included to enhance the search space exploration capabilities of GPs. In comparison to standard point mutation that randomly alters the values of the mutated genes, the proposed operator uses semantics to determine the appropriate value for each mutated gene. The proposed method converges on common Boolean benchmarks much faster than the conventional CGP and its variations while keeping the phenotype size relatively small.

Source link: https://doi.org/10.1007/s10710-021-09416-6

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions