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GECCO
2009
Springer
131views Optimization» more  GECCO 2009»
15 years 8 months ago
Rapid prototyping using evolutionary approaches: part 1
In this paper we describe a multi-objective problem solving approach, simultaneously minimizing average surface roughness Ra and build Time T, for object manufacturing by Rapid Pr...
Nikhil Padhye, Subodh Kalia
103
Voted
IJCAI
1993
15 years 4 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
2005
Springer
108views Optimization» more  GECCO 2005»
15 years 9 months ago
Some theoretical results about the computation time of evolutionary algorithms
This paper focuses on the computation time of evolutionary algorithms. First, some exact expressions of the mean first hitting times of general evolutionary algorithms in finite...
Lixin X. Ding, Jinghu Yu
131
Voted
GECCO
2005
Springer
157views Optimization» more  GECCO 2005»
15 years 9 months ago
Simple addition of ranking method for constrained optimization in evolutionary algorithms
During the optimization of a constrained problem using evolutionary algorithms (EAs), an individual in the population can be described using three important properties, i.e., obje...
Pei Yee Ho, Kazuyuki Shimizu
CEC
2008
IEEE
15 years 10 months ago
An investigation on evolutionary gradient search for multi-objective optimization
—Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and r...
Chi Keong Goh, Yew-Soon Ong, Kay Chen Tan, Eu Jin ...