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IJCAI
1993
13 years 10 months ago
Evolutionary Learning Strategy using Bug-Based Search
We introduce a new approach to GA (Genetic Algorithms) based problem solving. Earlier GAs did not contain local search (i.e. hill climbing) mechanisms, which led to optimization d...
Hitoshi Iba, Tetsuya Higuchi, Hugo de Garis, Taisu...
GECCO
2009
Springer
133views Optimization» more  GECCO 2009»
14 years 1 months ago
Evolutionary codings and operators for the terminal assignment problem
Given a collection of terminals, each with a demand, a collection of concentrators, each with a capacity, and costs of connecting the terminals to the concentrators, the terminal ...
Bryant A. Julstrom
IEEECIT
2009
IEEE
14 years 3 months ago
Clustering of Software Systems Using New Hybrid Algorithms
—Software clustering is a method for increasing software system understanding and maintenance. Software designers, first use MDG graph to model the structure of software system. ...
Ali Safari Mamaghani, Mohammad Reza Meybodi
GECCO
2007
Springer
167views Optimization» more  GECCO 2007»
14 years 2 months ago
An improved restricted growth function genetic algorithm for the consensus clustering of retinal nerve fibre data
This paper describes an extension to the Restricted Growth Function grouping Genetic Algorithm applied to the Consensus Clustering of a retinal nerve fibre layer data-set. Consens...
Stephen Swift, Allan Tucker, Jason Crampton, David...
CEC
2007
IEEE
13 years 10 months ago
Hybrid optimization using DIRECT, GA, and SQP for global exploration
— As there are many good optimization algorithms each with its own characteristics, it is very difficult to choose the best method for optimization problems. Thus, it is importa...
Satoru Hiwa, Tomoyuki Hiroyasu, Mitsunori Miki