Hybrid Evolutionary Code Generation Optimizing Both Functional Form and Parameter Values


Evolutionary computation (EC) is an effective tool in the optimization 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 behavior, 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 synthesize 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 optimization 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 optimize the parameters (constants) required by the synthesized 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.

  • Abstract
  • Introduction
  • Motivation
  • Prototype System for Function Synthesis
  • Conclusions and Future Work
  • References

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