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CEC
2005
IEEE
13 years 10 months ago
Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
Several approaches have been developed into evolutionary algorithms to deal with dynamic optimization problems, of which memory and random immigrants are two major schemes. This pa...
Shengxiang Yang
TEC
2008
165views more  TEC 2008»
13 years 4 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
CEC
2008
IEEE
13 years 11 months ago
A multi-agent based evolutionary algorithm in non-stationary environments
— In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolv...
Yang Yan, Hongfeng Wang, Dingwei Wang, Shengxiang ...
GECCO
2006
Springer
220views Optimization» more  GECCO 2006»
13 years 8 months ago
A comparative study of immune system based genetic algorithms in dynamic environments
Diversity and memory are two major mechanisms used in biology to keep the adaptability of organisms in the everchanging environment in nature. These mechanisms can be integrated i...
Shengxiang Yang
GECCO
2005
Springer
174views Optimization» more  GECCO 2005»
13 years 10 months ago
Diversity as a selection pressure in dynamic environments
Evolutionary algorithms (EAs) are widely used to deal with optimization problems in dynamic environments (DE) [3]. When using EAs to solve DE problems, we are usually interested i...
Lam Thu Bui, Jürgen Branke, Hussein A. Abbass