- author = "Peter Nordin",
- title = "Comparison of a compiling genetic programming system versus a connectionist approach",
- booktitle = "Handbook of Evolutionary Computation",
- publisher = "Oxford University Press",
- publisher_2 = "Institute of Physics Publishing",
- year = "1997",
- editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz",
- chapter = "section G1.5",
- keywords = "genetic algorithms, genetic programming, CGPS",
- ISBN = "0-7503-0392-1",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
- broken = "doi:10.1201/9781420050387.ptg",
-
URL = "
https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921",
- size = "7 pages",
- abstract = "This case study briefly presents the compiling genetic programming method and evaluates its performance against a neural network. Most genetic programming approaches use a technique where a problem specific language is executed by an interpreter. The individual code segments in the population are decoded at run time by a virtual machine. The disadvantage of this paradigm is that interpreting the program involves a large overhead. We have evaluated the idea of using the lowest-level native binary machine code as the individuals in the population. There is no intermediate language nor any interpreting steps. The genetic program that administers these machine code segments is written in C. The algorithm is steady state and uses a small tournament for selection. This approach has enhanced performance by up to 2000 times compared to a conventional system in an interpreting language. The increased performance is tested on a problem of symbolic regression of a classifier function in machine code. We evolve a machine code program that classifies Swedish words into nouns and non-nouns by spelling only. We compare the compiling genetic programming system (CGPS) with a neural network performing the same task. In our example, the results show superior performance of the CGPS compared to the connectionist approach. While the classification and generalization capabilities are equal, the training time is more than 200 times faster, the classification time 500 times faster, and the memory requirements at least ten times lower with the CGPS, as compared with the neural network.",
-
notes = "training times is 200 times faster, the classification
times 500 times faster
2100 Swedish words nouns v non-nouns. 2000x faster than lisp, 100x than C interpreter. Sun microsystems machine code",
