Techniques to improve exploration efficiency of parallel self-adaptive genetic algorithms by dispensing with iteration and synchronization

2006 ◽  
Vol 37 (14) ◽  
pp. 25-33 ◽  
Author(s):  
Eiichi Takashima ◽  
Yoshihiro Murata ◽  
Naoki Shibata ◽  
Minoru Ito
Keyword(s):  
Genetic Algorithms ◽  
Adaptive Genetic Algorithms ◽  
Self Adaptive

scholarly journals Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

2001 ◽  
Vol 9 (2) ◽  
pp. 197-221 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Hans-Georg Beyer
Keyword(s):  
Genetic Algorithms ◽  
Essential Feature ◽  
Function Optimization ◽  
Real Parameter ◽  
The Self ◽  
Test Problems ◽  
Adaptive Genetic Algorithms ◽  
Working Principle ◽  
Self Adaptation ◽  
Self Adaptive

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.

scholarly journals SAGAXSEARCH: AN XML INFORMATION RETRIEVAL MECHANISM USING SELF ADAPTIVE GENETIC ALGORITHMS

2005 ◽  
Vol 05 (04) ◽  
pp. 471-493 ◽  
Author(s):  
K. G. SRINIVASA ◽  
S. SHARATH ◽  
K. R. VENUGOPAL ◽  
M. PATNAIK
Keyword(s):  
Genetic Algorithms ◽  
Information Retrieval ◽  
Database Systems ◽  
Query Execution ◽  
Text Documents ◽  
Relationship Strength ◽  
Adaptive Genetic Algorithms ◽  
Xml Documents ◽  
Retrieval Mechanism ◽  
Self Adaptive

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient when retrieving relevant XML documents. In this paper, Self Adaptive Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed SAGAXsearch outperforms XSEarch3 and XRank20 with respect to accuracy and query execution time.

Modified adaptive genetic algorithms for solving 0/1 knapsack problems

2013 ◽  
Vol 32 (6) ◽  
pp. 1682-1684
Author(s):  
Na WANG ◽  
Feng-hong XIANG ◽  
Jian-lin MAO
Keyword(s):  
Genetic Algorithms ◽  
Knapsack Problems ◽  
Adaptive Genetic Algorithms
Sign in / Sign up

Export Citation Format

Share Document