The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Genetic Learning: Initialization and Representation Issues
Abstract
This chapter discusses the incorporation of genetic algorithms into machine learning. It does not present the principles of genetic algorithms (because it has been already done by many more or less large monographs) but rather focuses particularly on some important issues and enhancements of genetic algorithms design is faced by, namely: (a) the ways of initialization of a population for a genetic algorithm, (b) representation of chromosomes (individuals) in genetic algorithms (because it plays an influential role in the entire processing), and (c) discretization and fuzzification of numerical attributes for genetic algorithms (since they are not in its genuine form able to process these attributes). Furthermore, this chapter surveys new trends of dealing with the variable-length chromosomes and other issues related to the genetic learners. It concludes by discussing some directions of further improvement of the genetic learners, namely two topologies with the ‘meta’ level.
Related Content
|
Murray Eugene Jennex.
© 2020.
29 pages.
|
|
Ronald John Lofaro.
© 2020.
18 pages.
|
|
Mark E. Nissen.
© 2020.
23 pages.
|
|
Ronel Davel, Adeline S. A. Du Toit, Martie Mearns.
© 2020.
32 pages.
|
|
Murray Eugene Jennex.
© 2020.
23 pages.
|
|
Michael J. Zhang.
© 2020.
21 pages.
|
|
Toshali Dey, Susmita Mukhopadhyay.
© 2020.
23 pages.
|
|
|