- author = "Wilker Shane Bruce",
- title = "The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs",
- school = "School of Computer and Information Sciences, Nova Southeastern University",
- year = "1995",
- address = "3100 SW 9th Avenue, Fort Lauderdale, Florida 33315, USA",
- month = Dec,
- keywords = "genetic algorithms, genetic programming, memory",
-
URL = "
https://nsuworks.nova.edu/gscis_etd/430/",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce.thesis.ps.gz",
- size = "664 pages",
-
abstract = "Genetic programming is an automatic programming method
that creates computer programs to satisfy a software
designer's input/output specification through the
application of principles from genetics and
evolutionary biology. A population of programs is
maintained where each program is represented in the
chromosome data structure as a tree. Programs are
evaluated to determine their fitness in solving the
specified task. Simulated genetic operations like
crossover and mutation are probabilistically applied to
the more highly fit programs in the population to
generate new programs. These programs then replace
existing programs in the population according to the
principles of natural selection. The process repeats
until a correct program is found or an iteration limit
is reached.
This research concerns itself with the application of genetic programming to the generation of object-oriented programs. A new chromosome data structure is presented in which the entire set of methods associated with an object are stored as a set of program trees. Modified genetic operators that manipulate this new structure are defined. Indexed memory methods are used to allow the programs generated by the system to access and modify object memory. The result of these modifications to the standard genetic programming paradigm is a system that can simultaneously generate all of the methods associated with an object.
Experiments were performed to compare the sequential generation of object methods with two variants of simultaneous generation. The first variant used information about both method return values and object internal memory state in its fitness function. The second variant only used information about method return values. 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. It was also found that this technique is up to four orders of magnitude more computationally expensive in terms of number of individuals generated in the search than the sequential generation of the same set of methods on an individual basis.",
- notes = "Supervisor: Phillip M Admas",
