Now showing items 1-3 of 3
Evolutionary programming with q-Gaussian mutation for evolutionary optimization problems.
The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is ...
Hyper-selection in dynamic environments.
In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for ...
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 ...