Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Simultaneous evolution of programs and their control structures
Advances in genetic programming
Discovery of subroutines in genetic programming
Advances in genetic programming
Evolving recursive programs for tree search
Advances in genetic programming
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
A hybrid approach to automatic programming for the object-oriented programming paradigm
Proceedings of the 2007 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Coevolving programs and unit tests from their specification
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Evolving efficient list search algorithms
EA'09 Proceedings of the 9th international conference on Artificial evolution
Evolutionary repair of faulty software
Applied Soft Computing
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Co-evolutionary automatic programming for software development
Information Sciences: an International Journal
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A fundamental issue in evolutionary learning is the definition of the solution representation language. We present the application of Object Oriented Genetic Programming to the task of coevolving general recursive sorting algorithms along with their primitive representation alphabet. We report the computational effort required to evolve target solutions and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the induction of evolved method signatures (typed parameters and return type) can be realized through an evolutionary fitness-driven process. We also found that the evolutionary algorithm outperformed undirected random search, and that mutation performed better than crossover in this problem domain. The main result is that modular sorting algorithms can be evolved.