- author = "John Woodward",
- title = "{GA} or {GP}? That is not the question",
- booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
- editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
- pages = "1056--1063",
- year = "2003",
- publisher = "IEEE Press",
- address = "Canberra",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "8-12 " # dec,
- organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
- keywords = "genetic algorithms, genetic programming, Computer science, Evolutionary computation, High performance computing, Terminology, Tree data structures, data structures, search problems, GA, GP, No Free Lunch theorem, evolutionary computation, fixed length linear representation, variable size tree representation",
- ISBN = "0-7803-7804-0",
-
URL = "
http://www.cs.bham.ac.uk/~jrw/publications/2003/GAorGPthatisnotthequestion/cec032e.pdf",
-
DOI = "
doi:10.1109/CEC.2003.1299785",
- size = "8 pages",
-
abstract = "Genetic Algorithms (GAs) and Genetic Programming (GP)
are often considered as separate but related fields.
Typically, GAs use a fixed length linear
representation, whereas GP uses a variable size tree
representation. This paper argues that the differences
are unimportant. Firstly, variable length actually
means variable length up to some fixed limit, so can
really be considered as fixed length. Secondly, the
representations and genetic operators of GA and GP
appear different, however ultimately it is a population
of bit strings in the computers memory which is being
manipulated whether it is GA or GP which is being run
on the computer.
The important difference lies in the interpretation of the representation; if there is a one to one mapping between the description of an object and the object itself (as is the case with the representation of numbers), or a many to one mapping (as is the case with the representation of programs). This has ramifications for the validity of the No Free Lunch theorem, which is valid in the first case but not in the second. It is argued that due to the highly related nature of GAs and GP, that many of the empirical results discovered in one field will apply to the other field, for example maintaining high diversity in a population to improve performance.",
-
notes = "broken Oct 2019
http://tech.groups.yahoo.com/group/genetic_programming/message/5687
CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",
