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文件名称:CMA-ES
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- 上传时间:2013-09-13
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文件大小:31kb
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The optimization behavior of the self-adaptation
(SA) evolution strategy (ES) with intermediate multirecombination
(the (=I )-SA-ES) using isotropic mutations
is investigated on the general elliptic objective function. An
asymptotically exact quadratic progress rate formula is derived.
This is used to model the dynamical ES system by a set of
difference equations. The solutions of this system are used to
analytically calculate the optimal learning parameter . The
theoretical results are compared and validated by comparison
with real (=I )-SA-ES runs on typical elliptic test model
cases. The theoretical results clearly indicate that using a
model-independent learning parameter leads to suboptimal
performance of the (=I )-SA-ES on objective functions
with changing local condition numbers as often encountered in
practical problems with complex fitness landscapes.-The optimization behavior of the self-adaptation
(SA) evolution strategy (ES) with intermediate multirecombination
(the (=I )-SA-ES) using isotropic mutations
is investigated on the general elliptic objective function. An
asymptotically exact quadratic progress rate formula is derived.
This is used to model the dynamical ES system by a set of
difference equations. The solutions of this system are used to
analytically calculate the optimal learning parameter . The
theoretical results are compared and validated by comparison
with real (=I )-SA-ES runs on typical elliptic test model
cases. The theoretical results clearly indicate that using a
model-independent learning parameter leads to suboptimal
performance of the (=I )-SA-ES on objective functions
with changing local condition numbers as often encountered in
practical problems with complex fitness landscapes.
(SA) evolution strategy (ES) with intermediate multirecombination
(the (=I )-SA-ES) using isotropic mutations
is investigated on the general elliptic objective function. An
asymptotically exact quadratic progress rate formula is derived.
This is used to model the dynamical ES system by a set of
difference equations. The solutions of this system are used to
analytically calculate the optimal learning parameter . The
theoretical results are compared and validated by comparison
with real (=I )-SA-ES runs on typical elliptic test model
cases. The theoretical results clearly indicate that using a
model-independent learning parameter leads to suboptimal
performance of the (=I )-SA-ES on objective functions
with changing local condition numbers as often encountered in
practical problems with complex fitness landscapes.-The optimization behavior of the self-adaptation
(SA) evolution strategy (ES) with intermediate multirecombination
(the (=I )-SA-ES) using isotropic mutations
is investigated on the general elliptic objective function. An
asymptotically exact quadratic progress rate formula is derived.
This is used to model the dynamical ES system by a set of
difference equations. The solutions of this system are used to
analytically calculate the optimal learning parameter . The
theoretical results are compared and validated by comparison
with real (=I )-SA-ES runs on typical elliptic test model
cases. The theoretical results clearly indicate that using a
model-independent learning parameter leads to suboptimal
performance of the (=I )-SA-ES on objective functions
with changing local condition numbers as often encountered in
practical problems with complex fitness landscapes.
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下载文件列表
CMA-ES/
CMA-ES/Ackley.m
CMA-ES/CMAES.asv
CMA-ES/CMAES.m
CMA-ES/CMAES1.asv
CMA-ES/CMAES1.m
CMA-ES/ClearDups.m
CMA-ES/ComputeAveCost.m
CMA-ES/Conclude.m
CMA-ES/GA.m
CMA-ES/Init.m
CMA-ES/PopSort.m
CMA-ES/Rosenbrock.m
CMA-ES/Sphere.m
CMA-ES/Step.m
CMA-ES/picture for test/
CMA-ES/purecmaes.asv
CMA-ES/purecmaes.m
CMA-ES/purecmaes1.asv
CMA-ES/purecmaes1.m
CMA-ES/purecmaes_original.m
CMA-ES/readme.txt
CMA-ES/Ackley.m
CMA-ES/CMAES.asv
CMA-ES/CMAES.m
CMA-ES/CMAES1.asv
CMA-ES/CMAES1.m
CMA-ES/ClearDups.m
CMA-ES/ComputeAveCost.m
CMA-ES/Conclude.m
CMA-ES/GA.m
CMA-ES/Init.m
CMA-ES/PopSort.m
CMA-ES/Rosenbrock.m
CMA-ES/Sphere.m
CMA-ES/Step.m
CMA-ES/picture for test/
CMA-ES/purecmaes.asv
CMA-ES/purecmaes.m
CMA-ES/purecmaes1.asv
CMA-ES/purecmaes1.m
CMA-ES/purecmaes_original.m
CMA-ES/readme.txt