Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A comparative analysis of genetic programming
Advances in genetic programming
Coevolving functions in genetic programming
Journal of Systems Architecture: the EUROMICRO Journal - Special issue on evolutionary computing
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Algorithmics: The Spirit of Computing
Algorithmics: The Spirit of Computing
Generality and Difficulty in Genetic Programming: Evolving a Sort
Proceedings of the 5th International Conference on Genetic Algorithms
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Strongly typed genetic programming
Evolutionary Computation
An improved representation for evolving programs
Genetic Programming and Evolvable Machines
Evolving modular recursive sorting algorithms
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
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We peruse the idea of algorithmic design through Darwinian evolution, focusing on the problem of evolving list search algorithms. Specifically, we employ genetic programming (GP) to evolve iterative algorithms for searching for a given key in an array of integers. Our judicious design of an evolutionary language renders the evolution of linear-time search algorithms easy. We then turn to the far more difficult problem of logarithmic-time search, and show that our evolutionary system successfully handles this case. Subsequently, because our setup might be perceived as being geared towards the emergence of binary search, we generalize our genomic representation, allowing evolution to assemble its own useful functions via the mechanism of automatically defined functions (ADFs). We show that our approach routinely and repeatedly evolves general and correct efficient algorithms.