Now showing items 1-3 of 3
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-evolve to find optimum. All agents live in a lattice ...
Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.
(MIT Press., 2008)
In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The ...
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 approaches, such as the memory and multiple ...