Evolutionary algorithm with forced variation in multi-dimensional non-stationary environment

Authors:
Dariusz Wawrzyniak;Andrzej Obuchowicz
Affiliations:
Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland;Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland
Venue:
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Year:
2007

Citing 4
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Speeding Up Evolution through Learning: LEM

Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Evolutionary adaptation in non-stationary environments: a case study

PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper deals with an evolutionary algorithm which uses new methods for controlling the range of mutation. In order to significantly increase the efficiency in finding the optimum, it discovers and exploits knowledge about the state of population in the environment in the every generation. It allows to find the solution to be found both quickly and accurately. By dividing the population into objects dealing with different functions of optimization, it can simultaneously explore as well as exploit the solutions space. These abilities allow to increase the algorithm efficiency also in multi-dimensional environments.