Abstract
Genetic programming is a metaheuristic search method
that uses a population of variable-length computer
programs and a search strategy based on biological
evolution. The idea of automatic programming has long
been a goal of artificial intelligence, and genetic
programming presents an intuitive method for
automatically evolving programs. However, this method
is not without some potential drawbacks. Search using
procedural representations can be complex and
inefficient. In addition, variable sized solutions can
become unnecessarily large and difficult to
interpret.
The goal of this thesis is to understand the dynamics
of genetic programming that encourages efficient and
effective search. Toward this goal, the research
focuses on an important property of genetic programming
search: the population. The population is related to
many key aspects of the genetic programming algorithm.
In this programme of research, diversity is used to
describe and analyse populations and their effect on
search. A series of empirical investigations are
carried out to better understand the genetic
programming algorithm.
the relationship between diversity and search. The
effect of increased population diversity and a metaphor
of search are then examined. This is followed by an
investigation into the phenomenon of increased solution
size and problem difficulty. The research concludes by
examining the role of diverse individuals, particularly
the ability of diverse individuals to affect the search
process and ways of improving the genetic programming
algorithm.
(1) An analysis shows the complexity of the issues of
diversity and the relationship between diversity and
fitness, (2) The genetic programming search process is
characterised by using the concept of genetic lineages
and the sampling of structures and behaviours, (3) A
causal model of the varied rates of solution size
increase is presented, (4) A new, tunable problem
demonstrates the contribution of different population
members during search, and (5) An island model is
proposed to improve the search by speciating dissimilar
individuals into better-suited environments.
Currently, genetic programming is applied to a wide
range of problems under many varied contexts. From
artificial intelligence to operations research, the
results presented in this thesis will benefit
population-based search methods, methods based on the
concepts of evolution and search methods using
variable-length representations.
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