This thesis investigates the evolution and use of abstract data types within Genetic Programming (GP). In genetic programming the principles of natural evolution (fitness based selection and recombination) acts on program code to automatically generate computer programs. The research in this thesis is motivated by the observation from software engineering that data abstraction (e.g. via abstract data types) is essential in programs created by human programmers. We investigate whether abstract data types can be similarly beneficial to the automatic production of programs using GP. GP can automatically "evolve " programs which solve non-trivial problems but few experiments have been reported where the evolved programs explicitly manipulate memory and yet memory is an essential component of most computer programs. So far work on evolving programs that explicitly use memory has principally used either problem specific memory models or a simple indexed memory model consisting of a single global shared array. Whilst the latter is potentially sufficient to allow any computation to evolve, it is unstructured and allows complex interaction between parts of programs which weaken
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5012
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Genetic Algorithms
– Goldberg
- 1989
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868
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Handbook of Genetic Algorithms
– Davis
- 1991
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660
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Intelligence Without Reason
– Brooks
- 1991
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608
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Data Structures and Algorithms
– Aho, Hopcroft, et al.
- 1987
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334
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Genetic algorithms for multiobjective optimization: formulation, discussion and generalization
– Fonseca, Fleming
- 1993
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309
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An overview of evolutionary algorithms in multiobjective optimization
– Fonseca, Fleming
- 1995
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193
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J.H.: Genetic Algorithms, noise, and the sizing of populations
– Goldberg, Deb, et al.
- 1992
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182
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The Genetical Theory of Natural Selection
– Fisher
- 1930
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161
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A representation for adaptive generation of simple sequential programs
– Cramer
- 1985
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145
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The evolution of evolvability in genetic programming
– Altenberg
- 1994
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130
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Competitive environments evolve better solutions for complex tasks
– Angeline, Pollack
- 1993
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101
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Genetic programming and emergent intelligence
– Angeline
- 1994
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97
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A Sequential Niching Technique for Multimodal Function
– Beasley, Bull, et al.
- 1993
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97
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Species adaptation genetic algorithms: A basis for a continuing saga. In Toward a Practice of Autonomous Systems
– Harvey
- 1992
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91
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Massive multimodality, deception, and genetic algorithms (IlliGAL
– Goldberg, Deb, et al.
- 1992
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87
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The Schema Theorem and Price’s Theorem
– Altenberg
- 1995
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77
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Coevolving high-level representations
– Angeline, Pollack
- 1994
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72
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Toward a theory of evolution strategies: On the benefit of sex – the (µ/µ, λ)-theory
– Beyer
- 1995
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66
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A promising genetic algorithm approach to job-shop scheduling, rescheduling and open-shop scheduling problems
– Fang, Ross, et al.
- 1993
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63
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Deception considered harmful
– Grefenstette
- 1993
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55
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L.: A comparison of selection schemes used in genetic algorithms
– Blickle, Thiele
- 1995
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50
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Two Self-Adaptive Crossover Operators for Genetic Programming
– Angeline
- 1996
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49
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Genetic Programming and Redundancy
– Blickle, Thiele
- 1994
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44
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Strongly typed genetic programming in evolving cooperation strategies
– Haynes, Wainwright, et al.
- 1995
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40
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Type inheritance in strongly typed genetic programming
– Haynes, Schoenfeld, et al.
- 1996
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39
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Context preserving crossover in genetic programming
– D'Haeseleer
- 1994
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39
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Genetic synthesis of modular neural networks
– Gruau
- 1993
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38
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An adverse interaction between crossover and restricted tree depth in genetic programming
– Gathercole, Ross
- 1996
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35
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Evolutionary Algorithms and Emergent Intelligence
– Angeline
- 1993
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35
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Using genetic algorithms to search program spaces
– Jong
- 1987
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35
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The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions
– Handley
- 1994
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33
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Automatically defined features: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them
– Andre
- 1994
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29
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Evolving a team
– Haynes, Sen, et al.
- 1995
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28
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On the use of a directed acyclic graph to represent a population of computer programs
– Handley
- 1994
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27
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On using syntactic constraints with genetic programming
– Gruau
- 1996
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25
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Cellular encoding for interactive evolutionary robotics
– Gruau, Quatramaran
- 1997
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24
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Studies in Artificial Evolution
– Collins
- 1992
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23
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Evolving deterministic finite automata using cellular encoding
– Brave
- 1996
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22
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Genetic programming for pedestrians
– Banzhaf
- 1993
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22
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A promising hybrid GA/heuristic approach for open-shop scheduling problems
– Fang, Ross, et al.
- 1994
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21
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Through the looking-glass and what Alice found there
– Carroll
- 1980
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19
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The evolution of agents that build mental models and create simple plans using genetic programming
– Andre
- 1995
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19
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Hans-Paul Schwefel. A survey of evolution strategies
– Back, Hoffmeister
- 1991
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19
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Evolving recursive programs for tree search
– Brave
- 1996
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18
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Genetic Programming for the Acquisition of Double Auction Market Strategies
– Andrews, Prager
- 1994
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17
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Evolving compact solutions in genetic programming: A case study
– Blickle
- 1996
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16
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Reducing Epistasis in Combinatorial Problems by Expansive
– Beasley, Martin, et al.
- 1993
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15
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A study in program response and the negative effects of introns in genetic programming
– Andre, Teller
- 1996
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15
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Learning distributed reactive strategies by genetic programming for the general job shop problem
– Atlan, Bonnet, et al.
- 1994
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14
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Parallel genetic programming on a network of transputers
– Andre, Koza
- 1995
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