Evolving Modular Recursive Sorting Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@InProceedings{eurogp07:agapitos2,

• author = "Alexandros Agapitos and Simon M. Lucas",
• title = "Evolving Modular Recursive Sorting Algorithms",
• editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
• booktitle = "Proceedings of the 10th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "4445",
• year = "2007",
• address = "Valencia, Spain",
• month = "11-13 " # apr,
• pages = "301--310",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-71602-0",
• ISBN = "3-540-71602-5",
• DOI = "doi:10.1007/978-3-540-71605-1_28",
• abstract = "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.",
• notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007",

}

Genetic Programming entries for Alexandros Agapitos Simon M Lucas

Citations