Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The evolution of mental models
Advances in genetic programming
Machine Learning - Special issue on inductive transfer
Data structures and genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
A historical perspective on the evolution of executable structures
Fundamenta Informaticae
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
Machine Learning
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code
Proceedings of the 6th International Conference on Genetic Algorithms
Evolving Data Structures with Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Evolving Turing Machines for Biosequence Recognition and Analysis
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Evolving Turing Machines from Examples
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Simple Principles of Metalearning
Simple Principles of Metalearning
Algorithm evolution with internal reinforcement for signal understanding
Algorithm evolution with internal reinforcement for signal understanding
Toward simulated evolution of machine-language iteration
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolving 3d morphology and behavior by competition
Artificial Life
Extending genetic programming to evolve perceptron-like learning programs
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Automatically designing selection heuristics
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Does Chomsky complexity affect genetic programming computational requirements?
Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment
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We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are highly suited to the process; genetic code is an effective transmitter of information and crossover is an effective way to search through the viable combinations. Evolution is not without its limitations, which are pointed out, and it appears to be a highly effective problem solver; however we over-estimate the problem solving ability of evolution, as it is often trying to solve "self-imposed" survival problems. We are concerned with the evolution of Turing Equivalent programs (i.e. those with iteration and memory). Each of the mechanisms which make evolution work so well are examined from the perspective of program induction. Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated effectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.