• Corpus ID: 18725960

Category : Genetic ProgrammingThe Lawnmower Problem Revisited : Stack-Based Genetic Programming and Automatically

@inproceedings{Bruce2007CategoryG,
  title={Category : Genetic ProgrammingThe Lawnmower Problem Revisited : Stack-Based Genetic Programming and Automatically},
  author={Wilker Shane Bruce},
  year={2007}
}
Stack-based genetic programming is an alternative to Koza-style tree-based genetic programming that generates linear programs that are executed on a virtual machine using a FORTH-style operand stack instead of tree-based function calls. A stack-based genetic programming system was extended to include the ability to generate programs containing automatically deened functions. Experiments were run to test the system using Koza's lawnmower problem. The stack-based system using automatically deened… 

References

SHOWING 1-10 OF 17 REFERENCES

High-performance, parallel, stack-based genetic programming

The HiGP virtual machine and genetic programming algorithms are described and it is demonstrated that the system's performance on a symbolic regression problem can be solved with substantially less computational effort than can a traditional genetic programming system.

Evolving Data Structures Using Genetic Programming

It is shown that GP can simultaneously evolve all the operations of a data structure by implementing each such operation with its own independent program tree, and can automatically generate stacks and queues.

Automatic generation of object-oriented programs using genetic programming

It was found that simultaneous generation of methods is possible in the domain of simple collection objects both with and without the availability of internal memory state in the fitness function.

Genetic Programming II: Automatic Discovery of Reusable Programs.

This book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities.

Genetic programming - on the programming of computers by means of natural selection

  • J. Koza
  • Computer Science
    Complex adaptive systems
  • 1993
This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.

Genetic Evolution of Machine Language Software

Results are presented on: the feasibility of using this large operator set and architectural representation; and, the computations required to breed string outputting programs vs. the size of the string and the GP parameters employed.

Stack-based genetic programming

  • Tim Perkis
  • Computer Science
    Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence
  • 1994
A new GP system in which target programs run on a stack-based virtual machine is described, which has certain advantages in terms of efficiency and simplicity of implementation, and for certain problems, its effectiveness is shown to be comparable or superior to current methods.

Toward simulated evolution of machine-language iteration

It is shown that genetic programming can produce exact and general multiplication routines by synthesizing the necessary iterative control structures from primitive machine-language instructions.

Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code

A method to evolve Turing-complete programs for a register machine that enables the use of most program constructs, such as arithmetic operators, large indexed memory, automatic decomposition into subfunctions and subroutines (ADFs), conditional constructs i.e. if-then-else, jumps, loop structures, recursion, protected functions, string and list functions is developed.