- author = "Dale E. Courte",
- title = "Hybrid Evolutionary Code Generation Optimizing Both Functional Form and Parameter Values",
- booktitle = "ANNIE 2007, Intelligent Engineering Systems through Artificial Neural Networks",
- year = "2007",
- editor = "Cihan H. Dagli",
- volume = "17",
- address = "St. Louis, MO, USA",
- note = "Part III: Evolutionary Computation",
- keywords = "genetic algorithms, genetic programming, grammatical evolution",
-
DOI = "
doi:10.1115/1.802655.paper35",
-
abstract = "Evolutionary computation (EC) is an effective tool in
the optimisation of complex systems. It is desirable to
model such a system with appropriate computer commands
and parameter settings. Automated determination of both
commands and settings, based on observed system
behaviour, is a desirable goal.
Of the many forms of evolutionary computation, one recently developed discipline is that of grammatical evolution (GE). This approach can evolve executable functions in any computer language that can be represented in BNF form. The ability to synthesise arbitrary functions from a formal grammar is an attractive alternative to the expression tree generation of the more common genetic programming (GP) approach. However, the GE approach may not be ideal for the optimisation of any real-valued parameters of the functions generated. This work combines the use of grammatical evolution for function synthesis with the use of evolutionary programming (EP) to optimise the parameters (constants) required by the synthesised functions. These two evolutionary processes combine to explore a rich and complex search space of functional forms and floating point values. A prototype system is implemented and applied to the problem of function approximation.",
