Evolving Compact Solutions in Genetic Programming: A Case Study

@inproceedings{Blickle1996EvolvingCS,
  title={Evolving Compact Solutions in Genetic Programming: A Case Study},
  author={Tobias Blickle},
  booktitle={PPSN},
  year={1996}
}
Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y = g(x) that approximates a set of data points (x i ; yi). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from diierent domains. Furthermore… CONTINUE READING
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Explicitly de ned intronsand destructive crossover in genetic programming

Peter Nordin, Frank Francone, Wolfgang Banzhaf
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System identi cationusing structured genetic algorithms

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