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.

scholarly journals Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness

2020 ◽  
Vol 22 (3) ◽  
pp. 128-139
Author(s):  
Esra'a Alkafaween ◽  
Ahmad B. A. Hassanat
Keyword(s):  
Success Factors ◽  
Optimization Problems ◽  
Combinatorial Problems ◽  
Mutation Operator ◽  
Efficient Tool ◽  
Key Success Factors ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Symmetric Traveling Salesman Problem ◽  
Hybrid Mutation Operator

Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA. Various mutation operators exist to solve hard combinatorial problems such as the TSP. In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation. We also improve the existing “select best mutation” strategy using the proposed mutation. We conducted several experiments on twelve benchmark Symmetric traveling salesman problem (STSP) instances. The results of our experiments show the efficiency of the proposed mutation, particularly when we use it with some other mutations.

Optimum setting of PID controllers based on using evolutionary programming algorithm

2004 ◽  
Vol 27 (3) ◽  
pp. 439-442 ◽  
Author(s):  
Wei‐Der Chang ◽  
Jun‐Juh Yan
Keyword(s):  
Evolutionary Programming ◽  
Programming Algorithm ◽  
Pid Controllers

NEW APPROACH WITH EVOLUTIONARY PROGRAMMING ALGORITHM TO EMISSION CONSTRAINED ECONOMIC DISPATCH

2006 ◽  
Vol 26 (3) ◽  
Author(s):  
P. Somasundaram ◽  
R. Lakshmiramanan ◽  
K. Kuppusamy
Keyword(s):  
Economic Dispatch ◽  
Evolutionary Programming ◽  
Programming Algorithm ◽  
New Approach

QoT aware evolutionary programming algorithm for transparent optical networks

Optik ◽  
2013 ◽  
Vol 124 (23) ◽  
pp. 6391-6399 ◽  
Author(s):  
Urmila Bhanja ◽  
Sudipta Mahapatra ◽  
Rajarshi Roy
Keyword(s):  
Optical Networks ◽  
Evolutionary Programming ◽  
Programming Algorithm ◽  
Transparent Optical Networks
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