Multi Objective Symbolic Regression

  • C. J. Hinde
  • N. Chakravorti
  • A. A. West
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 513)


Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite athletes more information and better expressions can be generated than by using single, or even double objective functions. Symbolic regression, multi-objective, non dominated sorting, genetic programming.


Pareto Front Probable Outlier Round Trip Time Fitness Evaluation Maximum Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Loughborough UniversityLoughboroughUK

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