- author = "Tina Yu",
- title = "Hierachical Processing for Evolving Recursive and Modular Programs Using Higher Order Functions and Lambda Abstractions",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2001",
- volume = "2",
- number = "4",
- pages = "345--380",
- month = dec,
- keywords = "genetic algorithms, genetic programming, hierarchical processing, recursion, structure abstraction, higher-order functions, lambda abstraction, polymorphism, type systems",
- ISSN = "1389-2576",
-
URL = "
http://www.cs.mun.ca/~tinayu/Publications_files/gpem.pdf",
-
DOI = "
doi:10.1023/A:1012926821302",
- size = "36 pages",
- abstract = "We present a novel approach using higher-order functions and l abstraction to evolve recursive and modular programs. Moreover, a new term 'structure abstraction' is introduced to describe the property emerged from the higher-order function program structure. We test this technique on the general even-parity problem. The results indicate that this approach is very effective with the general even-parity problem due to the appropriate selection of the foldr higher-order function. Initially, foldr structure abstraction identify the promising area of the search space at generation zero. Once the population is within the promising area, foldr structure abstraction provides hierarchical processing for search. Consequently, solutions to the general even-parity problem are found very efficiently. We identify the limitations of this new approach and conclude that only when the appropriate higher-order function is selected that the benefits of structure abstraction show.",
-
notes = "STGP polymorphic general solution to even-n-parity
from 12 test cases.
Article ID: 386362",
