- author = "George W. Gerules",
- title = "Enhancing Scalability in Genetic Programming With Adaptable Constraints, Type Constraints and Automatically Defined Functions",
- school = "University of Missouri -- St. Louis",
- year = "2019",
- address = "612 Clark Ave, USA",
- month = "11 " # jul,
- email = "ggerules@gmail.com",
- keywords = "genetic algorithms, genetic programming, ADF, scalability, types, bloat control",
-
URL = "
https://irl.umsl.edu/dissertation/867/",
-
URL = "https://search.proquest.com/docview/2302013388?accountid=14511",
- size = "127 pages",
-
abstract = "Genetic Programming is a type of biological inspired
machine learning. It is composed of a population of
stochastic individuals. Those individuals can exchange
portions of themselves with others in the population
through the crossover operation that draws its
inspiration from biology. Other biologically inspired
operations include mutation and reproduction. The form
an individual takes can be many things. It, however, is
represented most of the time as a computer program.
Constructing correct efficient programs can be
notoriously difficult. Various grammar, typing,
function constraint, or counting mechanisms can guide
creation and evolution of those individuals. These
mechanisms can reduce search space and improve
scalability of genetic program solutions. Finding
correct combinations of individuals, however, can be
extremely challenging when using methods found in GP
such as Automatically Defined Functions or other
Architecturally Altering Operations.
This work extends and combines in a unique way previous work on Constrained Genetic Programming, Adaptive Constrained Genetic Programming and Automatically Defined Functions.
This dissertation shows, compared to previous stand alone mechanisms, that a new combination of genetic programming constraint mechanisms and Automatically Defined Functions improve scalability for a number of benchmark problems. The combination of constraint mechanisms include delayed max tree size per evolved generations, typing on the evolved programs, use of automatically defined functions, and use of adaptive heuristics for function and terminals on the evolved programs.
Initial results show that this combination of methodologies create smaller efficient individuals capable of handling larger problems. Moreover, this combined methodology works particularly well for constraints can be applied ahead of time.",
-
notes = "CGP2.1 CGPF2.1 lil-gp, Lawn Mower, Bumble Bee, MA.
HLDM Example, hGP Test Problems. For loop example for
ADI, For loop example for ADL, Recursion example for
ADR in C.
implementation lilgp1.03 (modified int to long for statistics variables) Frameworks created, CGPF2.1, ACGPF2.1
Source Code https://github.com/ggerules/lilgp1.03
Supervisor: Cezary Z. Janikow
Co-Advisor Uday Chakraborty",
