文件名称:CMA-ES
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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
......\Ackley.m
......\CMAES.asv
......\CMAES.m
......\CMAES1.asv
......\CMAES1.m
......\ClearDups.m
......\ComputeAveCost.m
......\Conclude.m
......\GA.m
......\Init.m
......\PopSort.m
......\Rosenbrock.m
......\Sphere.m
......\Step.m
......\picture for test
......\purecmaes.asv
......\purecmaes.m
......\purecmaes1.asv
......\purecmaes1.m
......\purecmaes_original.m
......\readme.txt