84,215 research outputs found
A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms
A simplex-like search method for bi-objective optimization
We describe a new algorithm for bi-objective optimization, similar to the Nelder Mead simplex
algorithm, widely used for single objective optimization. For diferentiable bi-objective functions on
a continuous search space, internal Pareto optima occur where the two gradient vectors point in
opposite directions. So such optima may be located by minimizing the cosine of the angle between
these vectors. This requires a complex rather than a simplex, so we term the technique the \cosine
seeking complex". An extra beneft of this approach is that a successful search identifes the direction
of the effcient curve of Pareto points, expediting further searches. Results are presented for some
standard test functions. The method presented is quite complicated and space considerations here
preclude complete details. We hope to publish a fuller description in another place
MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems
MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally
expensive, black-box, global optimization problems that may have continuous,
mixed-integer, or pure integer variables. Due to the black-box nature of the
objective function, derivatives are not available. Hence, surrogate models are
used as computationally cheap approximations of the expensive objective
function in order to guide the search for improved solutions. Due to the
computational expense of doing a single function evaluation, the goal is to
find optimal solutions within very few expensive evaluations. The multimodality
of the expensive black-box function requires an algorithm that is able to
search locally as well as globally. MATSuMoTo is able to address these
challenges. MATSuMoTo offers various choices for surrogate models and surrogate
model mixtures, initial experimental design strategies, and sampling
strategies. MATSuMoTo is able to do several function evaluations in parallel by
exploiting MATLAB's Parallel Computing Toolbox.Comment: 13 pages, 7 figure
Using Gaussian process regression for efficient parameter reconstruction
Optical scatterometry is a method to measure the size and shape of periodic
micro- or nanostructures on surfaces. For this purpose the geometry parameters
of the structures are obtained by reproducing experimental measurement results
through numerical simulations. We compare the performance of Bayesian
optimization to different local minimization algorithms for this numerical
optimization problem. Bayesian optimization uses Gaussian-process regression to
find promising parameter values. We examine how pre-computed simulation results
can be used to train the Gaussian process and to accelerate the optimization.Comment: 8 pages, 4 figure
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