109,597 research outputs found
Cumulative Step-size Adaptation on Linear Functions
The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation,
where the step size is adapted measuring the length of a so-called cumulative
path. The cumulative path is a combination of the previous steps realized by
the algorithm, where the importance of each step decreases with time. This
article studies the CSA-ES on composites of strictly increasing functions with
affine linear functions through the investigation of its underlying Markov
chains. Rigorous results on the change and the variation of the step size are
derived with and without cumulation. The step-size diverges geometrically fast
in most cases. Furthermore, the influence of the cumulation parameter is
studied.Comment: arXiv admin note: substantial text overlap with arXiv:1206.120
Cumulative Step-size Adaptation on Linear Functions: Technical Report
The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation,
where the step size is adapted measuring the length of a so-called cumulative
path. The cumulative path is a combination of the previous steps realized by
the algorithm, where the importance of each step decreases with time. This
article studies the CSA-ES on composites of strictly increasing with affine
linear functions through the investigation of its underlying Markov chains.
Rigorous results on the change and the variation of the step size are derived
with and without cumulation. The step-size diverges geometrically fast in most
cases. Furthermore, the influence of the cumulation parameter is studied.Comment: Parallel Problem Solving From Nature (2012
Cumulative Step-size Adaptation on Linear Functions: Technical Report
The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies the CSA-ES on composites of strictly increasing with affine linear functions through the investigation of its underlying Markov chains. Rigorous results on the change and the variation of the step size are derived with and without cumulation. The step-size diverges geometrically fast in most cases. Furthermore, the influence of the cumulation parameter is studied
Markov Chain Analysis of Cumulative Step-size Adaptation on a Linear Constrained Problem
This paper analyzes a (1, )-Evolution Strategy, a randomized
comparison-based adaptive search algorithm, optimizing a linear function with a
linear constraint. The algorithm uses resampling to handle the constraint. Two
cases are investigated: first the case where the step-size is constant, and
second the case where the step-size is adapted using cumulative step-size
adaptation. We exhibit for each case a Markov chain describing the behaviour of
the algorithm. Stability of the chain implies, by applying a law of large
numbers, either convergence or divergence of the algorithm. Divergence is the
desired behaviour. In the constant step-size case, we show stability of the
Markov chain and prove the divergence of the algorithm. In the cumulative
step-size adaptation case, we prove stability of the Markov chain in the
simplified case where the cumulation parameter equals 1, and discuss steps to
obtain similar results for the full (default) algorithm where the cumulation
parameter is smaller than 1. The stability of the Markov chain allows us to
deduce geometric divergence or convergence , depending on the dimension,
constraint angle, population size and damping parameter, at a rate that we
estimate. Our results complement previous studies where stability was assumed.Comment: Evolutionary Computation, Massachusetts Institute of Technology Press
(MIT Press): STM Titles, 201
Markov Chain Analysis of Evolution Strategies on a Linear Constraint Optimization Problem
This paper analyses a -Evolution Strategy, a randomised
comparison-based adaptive search algorithm, on a simple constraint optimisation
problem. The algorithm uses resampling to handle the constraint and optimizes a
linear function with a linear constraint. Two cases are investigated: first the
case where the step-size is constant, and second the case where the step-size
is adapted using path length control. We exhibit for each case a Markov chain
whose stability analysis would allow us to deduce the divergence of the
algorithm depending on its internal parameters. We show divergence at a
constant rate when the step-size is constant. We sketch that with step-size
adaptation geometric divergence takes place. Our results complement previous
studies where stability was assumed.Comment: Amir Hussain; Zhigang Zeng; Nian Zhang. IEEE Congress on Evolutionary
Computation, Jul 2014, Beijing, Chin
Cascades of Dynamical Transitions in an Adaptive Population
In an adaptive population which models financial markets and distributed
control, we consider how the dynamics depends on the diversity of the agents'
initial preferences of strategies. When the diversity decreases, more agents
tend to adapt their strategies together. This change in the environment results
in dynamical transitions from vanishing to non-vanishing step sizes. When the
diversity decreases further, we find a cascade of dynamical transitions for the
different signal dimensions, supported by good agreement between simulations
and theory. Besides, the signal of the largest step size at the steady state is
likely to be the initial signal.Comment: 4 pages, 8 figure
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