- author = "Takuya Ito and Hitoshi Iba and Satoshi Sato",
- title = "A Self-Tuning Mechanism for Depth-Dependent Crossover",
- booktitle = "Advances in Genetic Programming 3",
- publisher = "MIT Press",
- year = "1999",
- editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline",
- chapter = "16",
- pages = "377--399",
- address = "Cambridge, MA, USA",
- month = jun,
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-262-19423-6",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch16.pdf",
-
DOI = "
doi:10.7551/mitpress/1110.003.0021",
- abstract = "There are three genetic operators: crossover, mutation and reproduction in Genetic Programming (GP). Among these genetic operators, the crossover operator mainly contributes to searching for a solution program. Therefore, we aim at improving the program generation by extending the crossover operator. The normal crossover selects crossover points randomly and destroys building blocks. We think that building blocks can be protected by swapping larger substructures. In our former work, we proposed a depth-dependent crossover. The depth-dependent crossover protected building blocks and constructed larger building blocks easily by swapping shallower nodes. However, there was problem-dependent characteristics on the depth-dependent crossover, because the depth selection probability was fixed for all nodes in a tree. To solve this difficulty, we propose a self-tuning mechanism for the depth selection probability. We call this type of crossover a {"}self-tuning depth-dependent crossover{"}. We compare GP performances of the selftuning depthdependent crossover with performances of the original depth-dependent crossover. Our experimental results clarify the superiority of the self tuning depth dependent crossover.",
-
notes = "AiGP3 See http://cognet.mit.edu
11 mux, santa fe ant, 4-even parity, simulated robot",
