362,069 research outputs found
Prospects for computational steering of evolutionary computation
Currently, evolutionary computation (EC) typically takes place in batch mode: algorithms are run autonomously, with the user providing little or no intervention or guidance. Although it is rarely possible to specify in advance, on the basis of EC theory, the optimal evolutionary algorithm for a particular problem, it seems likely that experienced EC practitioners possess considerable tacit knowledge of how evolutionary algorithms work. In situations such as this, computational steering (ongoing, informed user intervention in the execution of an otherwise autonomous computational process) has been profitably exploited to improve performance and generate insights into computational processes. In this short paper, prospects for the computational steering of evolutionary computation are assessed, and a prototype example of computational steering applied to a coevolutionary algorithm is presented
Approximation algorithms for wavelet transform coding of data streams
This paper addresses the problem of finding a B-term wavelet representation
of a given discrete function whose distance from f is
minimized. The problem is well understood when we seek to minimize the
Euclidean distance between f and its representation. The first known algorithms
for finding provably approximate representations minimizing general
distances (including ) under a wide variety of compactly supported
wavelet bases are presented in this paper. For the Haar basis, a polynomial
time approximation scheme is demonstrated. These algorithms are applicable in
the one-pass sublinear-space data stream model of computation. They generalize
naturally to multiple dimensions and weighted norms. A universal representation
that provides a provable approximation guarantee under all p-norms
simultaneously; and the first approximation algorithms for bit-budget versions
of the problem, known as adaptive quantization, are also presented. Further, it
is shown that the algorithms presented here can be used to select a basis from
a tree-structured dictionary of bases and find a B-term representation of the
given function that provably approximates its best dictionary-basis
representation.Comment: Added a universal representation that provides a provable
approximation guarantee under all p-norms simultaneousl
A single-shot measurement of the energy of product states in a translation invariant spin chain can replace any quantum computation
In measurement-based quantum computation, quantum algorithms are implemented
via sequences of measurements. We describe a translationally invariant
finite-range interaction on a one-dimensional qudit chain and prove that a
single-shot measurement of the energy of an appropriate computational basis
state with respect to this Hamiltonian provides the output of any quantum
circuit. The required measurement accuracy scales inverse polynomially with the
size of the simulated quantum circuit. This shows that the implementation of
energy measurements on generic qudit chains is as hard as the realization of
quantum computation. Here a ''measurement'' is any procedure that samples from
the spectral measure induced by the observable and the state under
consideration. As opposed to measurement-based quantum computation, the
post-measurement state is irrelevant.Comment: 19 pages, transition rules for the CA correcte
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