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Creation of Numerical Constants in Robust Gene Expression Programming.

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Summary
This summary is machine-generated.

Genetic Programming (GP) struggles to create accurate numerical constants. A new numeric crossover operator for Robust Gene Expression Programming (RGEP) significantly improves accuracy without increasing computational cost.

Keywords:
constant creationdigit-wise crossoverephemeral random constantsgene expression programminggenetic algorithmsgenetic programminggenotype/phenotype evolutionary algorithmsnumeric crossovernumeric mutationsymbolic regression

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Area of Science:

  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Creating accurate numerical constants is a major challenge in Genetic Programming (GP).
  • Existing methods like local optimizers or numeric mutations have limitations in accuracy, complexity, or computational cost.

Purpose of the Study:

  • To introduce a novel numeric crossover operator for Robust Gene Expression Programming (RGEP).
  • To evaluate the effectiveness of this new operator in improving the accuracy of numerical constants generated by evolutionary algorithms.

Main Methods:

  • Implementation of a specialized numeric crossover operator within the RGEP framework.
  • Utilizing normalized least squares error as the fitness measure for evaluating solutions.
  • Comparison against existing numeric mutation operators on symbolic regression problems.

Main Results:

  • The proposed numeric crossover operator significantly outperforms existing numeric mutation operators in finding highly accurate solutions.
  • The operator demonstrates superior performance in symbolic regression tasks.
  • The method achieves better accuracy without increasing computational overhead.

Conclusions:

  • The novel numeric crossover operator offers a simple yet effective solution for enhancing numerical constant creation in RGEP.
  • This approach addresses a key limitation in evolutionary computation for problems requiring precise numerical values.
  • The operator provides a computationally inexpensive and highly accurate method for symbolic regression.