123 research outputs found
On Non-Elitist Evolutionary Algorithms Optimizing Fitness Functions with a Plateau
We consider the expected runtime of non-elitist evolutionary algorithms
(EAs), when they are applied to a family of fitness functions with a plateau of
second-best fitness in a Hamming ball of radius r around a unique global
optimum. On one hand, using the level-based theorems, we obtain polynomial
upper bounds on the expected runtime for some modes of non-elitist EA based on
unbiased mutation and the bitwise mutation in particular. On the other hand, we
show that the EA with fitness proportionate selection is inefficient if the
bitwise mutation is used with the standard settings of mutation probability.Comment: 14 pages, accepted for proceedings of Mathematical Optimization
Theory and Operations Research (MOTOR 2020). arXiv admin note: text overlap
with arXiv:1908.0868
Dissipative systems: uncontrollability, observability and RLC realizability
The theory of dissipativity has been primarily developed for controllable
systems/behaviors. For various reasons, in the context of uncontrollable
systems/behaviors, a more appropriate definition of dissipativity is in terms
of the dissipation inequality, namely the {\em existence} of a storage
function. A storage function is a function such that along every system
trajectory, the rate of increase of the storage function is at most the power
supplied. While the power supplied is always expressed in terms of only the
external variables, whether or not the storage function should be allowed to
depend on unobservable/hidden variables also has various consequences on the
notion of dissipativity: this paper thoroughly investigates the key aspects of
both cases, and also proposes another intuitive definition of dissipativity.
We first assume that the storage function can be expressed in terms of the
external variables and their derivatives only and prove our first main result
that, assuming the uncontrollable poles are unmixed, i.e. no pair of
uncontrollable poles add to zero, and assuming a strictness of dissipativity at
the infinity frequency, the dissipativities of a system and its controllable
part are equivalent. We also show that the storage function in this case is a
static state function.
We then investigate the utility of unobservable/hidden variables in the
definition of storage function: we prove that lossless autonomous behaviors
require storage function to be unobservable from external variables. We next
propose another intuitive definition: a behavior is called dissipative if it
can be embedded in a controllable dissipative {\em super-behavior}. We show
that this definition imposes a constraint on the number of inputs and thus
explains unintuitive examples from the literature in the context of
lossless/orthogonal behaviors.Comment: 26 pages, one figure. Partial results appeared in an IFAC conference
(World Congress, Milan, Italy, 2011
Fluctuation scaling in complex systems: Taylor's law and beyond
Complex systems consist of many interacting elements which participate in
some dynamical process. The activity of various elements is often different and
the fluctuation in the activity of an element grows monotonically with the
average activity. This relationship is often of the form "", where the exponent is predominantly in
the range . This power law has been observed in a very wide range of
disciplines, ranging from population dynamics through the Internet to the stock
market and it is often treated under the names \emph{Taylor's law} or
\emph{fluctuation scaling}. This review attempts to show how general the above
scaling relationship is by surveying the literature, as well as by reporting
some new empirical data and model calculations. We also show some basic
principles that can underlie the generality of the phenomenon. This is followed
by a mean-field framework based on sums of random variables. In this context
the emergence of fluctuation scaling is equivalent to some corresponding limit
theorems. In certain physical systems fluctuation scaling can be related to
finite size scaling.Comment: 33 pages, 20 figures, 2 tables, submitted to Advances in Physic
Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields
Spatially referenced data often have autocovariance functions with elliptical
isolevel contours, a property known as geometric anisotropy. The anisotropy
parameters include the tilt of the ellipse (orientation angle) with respect to
a reference axis and the aspect ratio of the principal correlation lengths.
Since these parameters are unknown a priori, sample estimates are needed to
define suitable spatial models for the interpolation of incomplete data. The
distribution of the anisotropy statistics is determined by a non-Gaussian
sampling joint probability density. By means of analytical calculations, we
derive an explicit expression for the joint probability density function of the
anisotropy statistics for Gaussian, stationary and differentiable random
fields. Based on this expression, we obtain an approximate joint density which
we use to formulate a statistical test for isotropy. The approximate joint
density is independent of the autocovariance function and provides conservative
probability and confidence regions for the anisotropy parameters. We validate
the theoretical analysis by means of simulations using synthetic data, and we
illustrate the detection of anisotropy changes with a case study involving
background radiation exposure data. The approximate joint density provides (i)
a stand-alone approximate estimate of the anisotropy statistics distribution
(ii) informed initial values for maximum likelihood estimation, and (iii) a
useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure
A.N. Kolmogorov’s defence of Mendelism
In 1939 N.I. Ermolaeva published the results of an experiment which repeated parts of Mendel’s classical experiments. On the basis of her experiment she concluded that Mendel’s principle that self-pollination of hybrid plants gave rise to segregation proportions 3:1 was false. The great probability theorist A.N. Kolmogorov reviewed Ermolaeva’s data using a test, now referred to as Kolmogorov’s, or Kolmogorov-Smirnov, test, which he had proposed in 1933. He found, contrary to Ermolaeva, that her results clearly confirmed Mendel’s principle. This paper shows that there were methodological flaws in Kolmogorov’s statistical analysis and presents a substantially adjusted approach, which confirms his conclusions. Some historical commentary on the Lysenko-era background is given, to illuminate the relationship of the disciplines of genetics and statistics in the struggle against the prevailing politically-correct pseudoscience in the Soviet Union. There is a Brazilian connection through the person of Th. Dobzhansky
Statistical modeling of ground motion relations for seismic hazard analysis
We introduce a new approach for ground motion relations (GMR) in the
probabilistic seismic hazard analysis (PSHA), being influenced by the extreme
value theory of mathematical statistics. Therein, we understand a GMR as a
random function. We derive mathematically the principle of area-equivalence;
wherein two alternative GMRs have an equivalent influence on the hazard if
these GMRs have equivalent area functions. This includes local biases. An
interpretation of the difference between these GMRs (an actual and a modeled
one) as a random component leads to a general overestimation of residual
variance and hazard. Beside this, we discuss important aspects of classical
approaches and discover discrepancies with the state of the art of stochastics
and statistics (model selection and significance, test of distribution
assumptions, extreme value statistics). We criticize especially the assumption
of logarithmic normally distributed residuals of maxima like the peak ground
acceleration (PGA). The natural distribution of its individual random component
(equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized
extreme value. We show by numerical researches that the actual distribution can
be hidden and a wrong distribution assumption can influence the PSHA negatively
as the negligence of area equivalence does. Finally, we suggest an estimation
concept for GMRs of PSHA with a regression-free variance estimation of the
individual random component. We demonstrate the advantages of event-specific
GMRs by analyzing data sets from the PEER strong motion database and estimate
event-specific GMRs. Therein, the majority of the best models base on an
anisotropic point source approach. The residual variance of logarithmized PGA
is significantly smaller than in previous models. We validate the estimations
for the event with the largest sample by empirical area functions. etc
Lévy patterns in seabirds are multifaceted describing both spatial and temporal patterning
BACKGROUND: The flight patterns of albatrosses and shearwaters have become a touchstone for much of Lévy flight research, spawning an extensive field of enquiry. There is now compelling evidence that the flight patterns of these seabirds would have been appreciated by Paul Lévy, the mathematician after whom Lévy flights are named. Here we show that Lévy patterns (here taken to mean spatial or temporal patterns characterized by distributions with power-law tails) are, in fact, multifaceted in shearwaters being evident in both spatial and temporal patterns of activity. RESULTS: We tested for Lévy patterns in the at-sea behaviours of two species of shearwater breeding in the North Atlantic Ocean (Calonectris borealis) and the Mediterranean sea (C. diomedea) during their incubating and chick-provisioning periods. We found that distributions of flight durations, on/in water durations and inter-dive time-intervals have power-law tails and so bear the hallmarks of Lévy patterns. CONCLUSIONS: The occurrence of these statistical laws is remarkable given that bird behaviours are strongly shaped by an individual’s motivational state and by complex environmental interactions. Our observations could take Lévy patterns as models of animal behaviour to a new level by going beyond the characterisation of spatial movements to characterise how different behaviours are interwoven throughout daily animal life
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