Exploring self-adaptive methods to improve the efficiency of generating approximate solutions to traveling salesman problems using evolutionary programming

1997 ◽  
pp. 361-371 ◽  
Author(s):  
Kumar Chellapilla ◽  
David B. Fogel
Keyword(s):  
Approximate Solutions ◽  
Evolutionary Programming ◽  
Adaptive Methods ◽  
Traveling Salesman ◽  
Traveling Salesman Problems ◽  
Self Adaptive

K-means Clustering Algorithm and Meta-heuristics for Multiple Traveling Salesman Problems

2009 ◽  
Vol 4 (2) ◽  
pp. 16-32
Author(s):  
R. Nallusamy ◽  
K. Duraiswamy ◽  
R. Dhanalaksmi ◽  
P. Parthiban
Keyword(s):  
Clustering Algorithm ◽  
Traveling Salesman ◽  
Traveling Salesman Problems

A Novel Discovery Technique on Attribute Weight of Engine CBR Design System

2010 ◽  
Vol 97-101 ◽  
pp. 3714-3717 ◽  
Author(s):  
Wei Yan ◽  
Qi Gao ◽  
Zheng Gang Liu ◽  
Shan Hui Zhang ◽  
Yu Ping Hu
Keyword(s):  
Evolutionary Programming ◽  
Programming Algorithm ◽  
Design System ◽  
Reference Case ◽  
Attribute Weight ◽  
Case Base ◽  
Mutation Strategies ◽  
Group Self ◽  
Gauss Operator ◽  
Self Adaptive

An improved multi-group self-adaptive evolutionary programming Algorithm is used to get adapt attribute weight for CBR system. Firstly, this paper analyses the adaptability function based on reference case base REF and testing case base TEST, develops a novel Bi-group self-adaptive evolutionary programming that overcome the lack of conventional evolutionary programming. In this Novel algorithm, evolution of Cauchy operator and Gauss operator are parallel performed with different mutation strategies, and the Gauss operator owns the ability of self-adaptation according to the variation of adaptability function. Information is exchanged when sub-groups are reorganized. Experiment results prove the validity of self-adaptive Algorithm and CBR design system is used successfully in engine design process.

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