15,716 research outputs found
Particle algorithms for optimization on binary spaces
We discuss a unified approach to stochastic optimization of pseudo-Boolean
objective functions based on particle methods, including the cross-entropy
method and simulated annealing as special cases. We point out the need for
auxiliary sampling distributions, that is parametric families on binary spaces,
which are able to reproduce complex dependency structures, and illustrate their
usefulness in our numerical experiments. We provide numerical evidence that
particle-driven optimization algorithms based on parametric families yield
superior results on strongly multi-modal optimization problems while local
search heuristics outperform them on easier problems
FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code
We present FitSKIRT, a method to efficiently fit radiative transfer models to
UV/optical images of dusty galaxies. These images have the advantage that they
have better spatial resolution compared to FIR/submm data. FitSKIRT uses the
GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo
radiative transfer code. Genetic algorithms prove to be a valuable tool in
handling the multi- dimensional search space as well as the noise induced by
the random nature of the Monte Carlo radiative transfer code. FitSKIRT is
tested on artificial images of a simulated edge-on spiral galaxy, where we
gradually increase the number of fitted parameters. We find that we can recover
all model parameters, even if all 11 model parameters are left unconstrained.
Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral
galaxy NGC4013. This galaxy has been modeled previously by other authors using
different combinations of radiative transfer codes and optimization methods.
Given the different models and techniques and the complexity and degeneracies
in the parameter space, we find reasonable agreement between the different
models. We conclude that the FitSKIRT method allows comparison between
different models and geometries in a quantitative manner and minimizes the need
of human intervention and biasing. The high level of automation makes it an
ideal tool to use on larger sets of observed data.Comment: 14 pages, 10 figures; accepted for publication in Astronomy and
Astrophysic
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning
pipelines by jointly selecting algorithms and their appropriate
hyper-parameters for all steps in supervised learning pipelines. This black-box
(gradient-free) optimization with mixed integer & continuous variables is a
challenging problem. We propose a novel AutoML scheme by leveraging the
alternating direction method of multipliers (ADMM). The proposed framework is
able to (i) decompose the optimization problem into easier sub-problems that
have a reduced number of variables and circumvent the challenge of mixed
variable categories, and (ii) incorporate black-box constraints along-side the
black-box optimization objective. We empirically evaluate the flexibility (in
utilizing existing AutoML techniques), effectiveness (against open source
AutoML toolkits),and unique capability (of executing AutoML with practically
motivated black-box constraints) of our proposed scheme on a collection of
binary classification data sets from UCI ML& OpenML repositories. We observe
that on an average our framework provides significant gains in comparison to
other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical
advantages of this framework
Adaptive Ranking Based Constraint Handling for Explicitly Constrained Black-Box Optimization
A novel explicit constraint handling technique for the covariance matrix
adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint
handling exhibits two invariance properties. One is the invariance to arbitrary
element-wise increasing transformation of the objective and constraint
functions. The other is the invariance to arbitrary affine transformation of
the search space. The proposed technique virtually transforms a constrained
optimization problem into an unconstrained optimization problem by considering
an adaptive weighted sum of the ranking of the objective function values and
the ranking of the constraint violations that are measured by the Mahalanobis
distance between each candidate solution to its projection onto the boundary of
the constraints. Simulation results are presented and show that the CMA-ES with
the proposed constraint handling exhibits the affine invariance and performs
similarly to the CMA-ES on unconstrained counterparts.Comment: 9 page
Multiobjective synchronization of coupled systems
Copyright @ 2011 American Institute of PhysicsSynchronization of coupled chaotic systems has been a subject of great interest and importance, in theory but also various fields of application, such as secure communication and neuroscience. Recently, based on stability theory, synchronization of coupled chaotic systems by designing appropriate coupling has been widely investigated. However, almost all the available results have been focusing on ensuring the synchronization of coupled chaotic systems with as small coupling strengths as possible. In this contribution, we study multiobjective synchronization of coupled chaotic systems by considering two objectives in parallel, i. e., minimizing optimization of coupling strength and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach. The constraints on the coupling form are also investigated by formulating the problem into a multiobjective constraint problem. We find that the proposed evolutionary method can outperform conventional adaptive strategy in several respects. The results presented in this paper can be extended into nonlinear time-series analysis, synchronization of complex networks and have various applications
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