Influence of (P)rngs Onto Gpa-Es Behaviors

Brandejsky, Tomas

doi:10.14311/nnw.2017.27.033

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Raidl and Gunther in [11] introduced HGP (hybrid genetic programming), added weights to the top-level tree members and optimized them using a robust least squares method. For example, gradient descent [12,13], simulated annealing combined with the simplex method [14], particle swarm optimization (PSO) [15], multiple regression in the STROGANOFF method [16,17], evolutionary strategies [13,18,19], genetic algorithms [13], self-organizing migrating algorithm (SOMA) [13,20], the Bison Seeker algorithm [21], and non-linear optimization using the Levenberg-Marquardt algorithm [22,23] can be used to optimize the constants. There are many modern approaches for GP optimization.…”

Section: Optimization Of Genetic Programming and Symbolic Regressionmentioning

confidence: 99%

Two-layer genetic programming

Merta¹,

Brandejsky²

2022

NNW

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.

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Section: Optimization Of Genetic Programming and Symbolic Regressionmentioning

confidence: 99%

Two-layer genetic programming

Merta¹,

Brandejsky²

2022

NNW

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“…Many artificial intelligence methods are dependent on the quality of the used random generator (function), but, in this case, where data are used for a neural network, which provides interpolation and noise reduction, a simpler generator can be used [6].…”

Section: Generation Of the Datamentioning

confidence: 99%

Neural Network for the identification of a functional dependence using data preselection

Hlaváč¹

2021

NNW

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A neural network can be used in the identification of a given functional dependency. An undetermined problem (with more degrees of freedom) has to be converted to a determined one by adding other conditions. This is easy for a well-defined problem, described by a theoretical functional dependency; in this case, no identification (using a neural network) is necessary. The article describes how to apply a fitness (or a penalty) function directly to the data, before a neural network is trained. As a result, the trained neural network is near to the best possible solution according to the selected fitness function. In comparison to implementing the fitness function during the training of the neural network, the method described here is simpler and more reliable. The new method is demonstrated on the kinematics control of a redundant 2D manipulator.

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“…In this case, the method used in [2] or [5] is implemented. The starting population is generated as in the previous case.…”

Section: Genetic Algorithms With Tournament Selectionmentioning

confidence: 99%

Genetic Programming With Either Stochastic or Deterministic Constant Evaluation

Hlaváč¹

2018

NNW

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Constant evaluation is a key problem for symbolic regression, one solved by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.

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Influence of (P)rngs Onto Gpa-Es Behaviors

Cited by 3 publications

References 11 publications

Two-layer genetic programming

Two-layer genetic programming

Neural Network for the identification of a functional dependence using data preselection

Genetic Programming With Either Stochastic or Deterministic Constant Evaluation

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