This research investigates the integration of evolutionary techniques for symbolic
regression. In particular the genetic programming paradigm is used together with other
evolutionary computational techniques to develop novel approaches to the improvement of
areas of simple preliminary design software using empirical data sets. It is shown that within
this problem domain, conventional genetic programming suffers from several limitations,
which are overcome by the introduction of an improved genetic programming strategy
based on node complexity values, and utilising a steady state algorithm with subpopulations.
A further extension to the new technique is introduced which incorporates a
genetic algorithm to aid the search within continuous problem spaces, increasing the
robustness of the new method. The work presented here represents an advance in the Geld
of genetic programming for symbolic regression with significant improvements over the
conventional genetic programming approach. Such improvement is illustrated by extensive
experimentation utilising both simple test functions and real-world design examples
