skip to main content

Title: Messy genetic algorithms: Recent developments

Messy genetic algorithms define a rare class of algorithms that realize the need for detecting appropriate relations among members of the search domain in optimization. This paper reviews earlier works in messy genetic algorithms and describes some recent developments. It also describes the gene expression messy GA (GEMGA)--an {Omicron}({Lambda}{sup {kappa}}({ell}{sup 2} + {kappa})) sample complexity algorithm for the class of order-{kappa} delineable problems (problems that can be solved by considering no higher than order-{kappa} relations) of size {ell} and alphabet size {Lambda}. Experimental results are presented to demonstrate the scalability of the GEMGA.
Authors:
 [1]
  1. Los Alamos National Lab., NM (United States). Computational Science Methods Group
Publication Date:
OSTI Identifier:
378868
Report Number(s):
LA-UR--96-2412; CONF-9607153--2
ON: TI96014240; TRN: AHC29620%%81
DOE Contract Number:
W-7405-ENG-36
Resource Type:
Technical Report
Resource Relation:
Conference: NATO ASI meeting, Gran de Canaria (Spain), 1-5 Jul 1996; Other Information: PBD: [1996]
Research Org:
Los Alamos National Lab., NM (United States)
Sponsoring Org:
USDOE, Washington, DC (United States)
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; OPERATIONS RESEARCH; ALGORITHMS; OPTIMIZATION; RESEARCH PROGRAMS; SCALING LAWS; EXPERIMENTAL DATA; FUNCTIONS