Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Competitive Evolution: A Natural Approach to Operator Selection
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
In this paper, we describe the use of an evolutionary algorithm (EA) to solve dynamic control optimization problems in engineering. In this class of problems, a set of control variables must be manipulated over time to optimize the outcome, which is obtained by solving a set of differential equations for the state variables. A new problem-specific technique, progressive step reduction (PSR), is shown to considerably improve the efficiency of the algorithm for this application. Factorial experimentation and rigorous statistical analysis are used to determine the effects of PSR and tune the parameters of the algorithm.