Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions [3 citations — 1 self]
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author = {Alan Robinson and Jaime Davila and Mark Feinstein},
title = {Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions},
year = {}
}
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Abstract:
Investigates techniques designed to allow genetic programming to evolve significantly more complex, modular, and functionally expressive code. Rather then developing a system from scratch, the research in this document builds upon the PushGP system developed by Spector (2001). PushGP uses a stack-based language with multiple stacks for operating on different data types. One stack stores program code and allows for interactive construction and modification of executable functions, modules, and control structures as the main program executes. The primary question addressed is what sort of modularity and structure evolve when their very composition arises from the evolutionary modifications of program code, rather than from external parsing on the part of genetic programming systems. The secondary question is how the computational effort of this system varies in comparison to more traditional genetic programming systems, (like Kozas GP system with automatically defined functions).
