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GECCO
2008
Springer
186views Optimization» more  GECCO 2008»
13 years 9 months ago
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...
GECCO
2005
Springer
157views Optimization» more  GECCO 2005»
14 years 2 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
TEC
2008
165views more  TEC 2008»
13 years 8 months ago
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several app...
Shengxiang Yang, Xin Yao
GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
14 years 1 months ago
An Improved Diversity Mechanism for Solving Constrained Optimization Problems Using a Multimembered Evolution Strategy
This paper presents an improved version of a simple evolution strategy (SES) to solve global nonlinear optimization problems. As its previous version, the approach does not require...
Efrén Mezura-Montes, Carlos A. Coello Coell...
CEC
2010
IEEE
13 years 9 months ago
Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails
Abstract-- Recently, the research on quantum-inspired evolutionary algorithms (QEA) has attracted some attention in the area of evolutionary computation. QEA use a probabilistic re...
Hui Li, Dario Landa Silva, Xavier Gandibleux