The impact of population sizes and diversity on the adaptability of evolution strategies in dynamic environments. Schönemann, L. In Congress on Evolutionary Computation, CEC2004, volume 2, pages 1270--1277, 2004.
The impact of population sizes and diversity on the adaptability of evolution strategies in dynamic environments [link]Paper  doi  abstract   bibtex   
In time-dependent optimization problems the main task for a problem solver is not to find a good solution, but to track the moving best solution. It is well-known that evolutionary algorithms (EA) can cope with this requirement. A main attribute of many EA is the self-adaptability. The functioning of this feature depends on the setting of several EA parameters. In case of evolution strategies it is still unknown under which conditions the algorithm is able to converge against the optimum. Our investigations concern different population sizes as well as the correlation between the best function value and the diversity of the population on some selected test functions.
@inproceedings{schonemann_impact_2004,
	title = {The impact of population sizes and diversity on the adaptability of evolution strategies in dynamic environments},
	volume = {2},
	isbn = {0780385152},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1331043},
	doi = {10.1109/CEC.2004.1331043},
	abstract = {In time-dependent optimization problems the main task for a problem solver is not to find a good solution, but to track the moving best solution. It is well-known that evolutionary algorithms (EA) can cope with this requirement. A main attribute of many EA is the self-adaptability. The functioning of this feature depends on the setting of several EA parameters. In case of evolution strategies it is still unknown under which conditions the algorithm is able to converge against the optimum. Our investigations concern different population sizes as well as the correlation between the best function value and the diversity of the population on some selected test functions.},
	booktitle = {Congress on {Evolutionary} {Computation}, {CEC}2004},
	author = {Schönemann, Lutz},
	year = {2004},
	keywords = {Average best function value (ABFV), Computer science, Dynamic environments, Evolutionary algorithms, Function evaluation, Genetic algorithms, Optimization, Optimization problem, Parameter estimation, Population size, Population statistics, Problem solving, Set theory},
	pages = {1270--1277}
}

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