489 research outputs found
Quasirandom Rumor Spreading: An Experimental Analysis
We empirically analyze two versions of the well-known "randomized rumor
spreading" protocol to disseminate a piece of information in networks. In the
classical model, in each round each informed node informs a random neighbor. In
the recently proposed quasirandom variant, each node has a (cyclic) list of its
neighbors. Once informed, it starts at a random position of the list, but from
then on informs its neighbors in the order of the list. While for sparse random
graphs a better performance of the quasirandom model could be proven, all other
results show that, independent of the structure of the lists, the same
asymptotic performance guarantees hold as for the classical model. In this
work, we compare the two models experimentally. This not only shows that the
quasirandom model generally is faster, but also that the runtime is more
concentrated around the mean. This is surprising given that much fewer random
bits are used in the quasirandom process. These advantages are also observed in
a lossy communication model, where each transmission does not reach its target
with a certain probability, and in an asynchronous model, where nodes send at
random times drawn from an exponential distribution. We also show that
typically the particular structure of the lists has little influence on the
efficiency.Comment: 14 pages, appeared in ALENEX'0
Online Selection of CMA-ES Variants
In the field of evolutionary computation, one of the most challenging topics
is algorithm selection. Knowing which heuristics to use for which optimization
problem is key to obtaining high-quality solutions. We aim to extend this
research topic by taking a first step towards a selection method for adaptive
CMA-ES algorithms. We build upon the theoretical work done by van Rijn
\textit{et al.} [PPSN'18], in which the potential of switching between
different CMA-ES variants was quantified in the context of a modular CMA-ES
framework.
We demonstrate in this work that their proposed approach is not very
reliable, in that implementing the suggested adaptive configurations does not
yield the predicted performance gains. We propose a revised approach, which
results in a more robust fit between predicted and actual performance. The
adaptive CMA-ES approach obtains performance gains on 18 out of 24 tested
functions of the BBOB benchmark, with stable advantages of up to 23\%. An
analysis of module activation indicates which modules are most crucial for the
different phases of optimizing each of the 24 benchmark problems. The module
activation also suggests that additional gains are possible when including the
(B)IPOP modules, which we have excluded for this present work.Comment: To appear at Genetic and Evolutionary Computation Conference
(GECCO'19) Appendix will be added in due tim
Contribution to the industry-specific identification and selection of a business model in machinery and equipment industry
Industry 4.0 introduces a paradigm shift that will lead to changes of business models in many e. Today, industrial companies are gradually transforming their traditional and transaction-based business models into new business models made possible by cyber-physical systems. New business models such as as-a-service or platform-based business models emerge. This change brings enormous opportunities, but also many risks for the manufacturing industry. Many companies are faced with the problem of choosing from the multitude of new business models. The business model development to be found in literature primarily follows the needs of the customer. Machinery and equipment industry is a particularly interesting sector since more than 50% of the customers in machinery and equipment industry come from the same sector.
This paper develops a process model for the industry-specific selection of business models. The model includes the following questions: Which business models can be selected in the field of machinery and equipment industry? Which possible goals can be pursued with the respective business models? Which criteria are useful for deciding on the respective business model
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
A key challenge in the application of evolutionary algorithms in practice is
the selection of an algorithm instance that best suits the problem at hand.
What complicates this decision further is that different algorithms may be best
suited for different stages of the optimization process. Dynamic algorithm
selection and configuration are therefore well-researched topics in
evolutionary computation. However, while hyper-heuristics and parameter control
studies typically assume a setting in which the algorithm needs to be chosen
while running the algorithms, without prior information, AutoML approaches such
as hyper-parameter tuning and automated algorithm configuration assume the
possibility of evaluating different configurations before making a final
recommendation. In practice, however, we are often in a middle-ground between
these two settings, where we need to decide on the algorithm instance before
the run ("oneshot" setting), but where we have (possibly lots of) data
available on which we can base an informed decision.
We analyze in this work how such prior performance data can be used to infer
informed dynamic algorithm selection schemes for the solution of pseudo-Boolean
optimization problems. Our specific use-case considers a family of genetic
algorithms.Comment: Submitted for review to GECCO'2
Quasirandom Rumor Spreading
We propose and analyze a quasirandom analogue of the classical push model for disseminating information in networks (ârandomized rumor spreadingâ). In the classical model, in each round, each informed vertex chooses a neighbor at random and informs it, if it was not informed before. It is known that this simple protocol succeeds in spreading a rumor from one vertex to all others within
O
(log
n
) rounds on complete graphs, hypercubes, random regular graphs, ErdĆs-RĂ©nyi random graphs, and Ramanujan graphs with probability 1 â
o
(1). In the quasirandom model, we assume that each vertex has a (cyclic) list of its neighbors. Once informed, it starts at a random position on the list, but from then on informs its neighbors in the order of the list. Surprisingly, irrespective of the orders of the lists, the above-mentioned bounds still hold. In some cases, even better bounds than for the classical model can be shown.
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Pricing of Content Services â An Empirical Investigation of Music as a Service
In the last years new concepts of digital music distribution have been developed. One of them is Music as a Service, which provides music streaming over the internet as a service - without transferring ownership for the content. This differentiates Music as a Service from Download to Own, which is used by music download platforms predominantly and is the most widely studied concept in academic research. The aim of this paper is to receive first research implications about customersâ attitudes towards MaaS.
Based on an empirical survey of 132 Music as a Service users, this research explores the effects of product configurations on consumersâ utility and their willingness to pay (WTP) for premium offers. We can show that next to price, contract duration and music quality as the most important product attributes, there is a high WTP for overcoming insufficient mobile internet coverage
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a given
optimization problem is an important task in evolutionary computation. We
compare in this work the results of four different hyperparameter tuning
approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean
optimization problems. More precisely, we compare previously obtained results
from a grid search with those obtained from three automated configuration
techniques: iterated racing, mixed-integer parallel efficient global
optimization, and mixed-integer evolutionary strategies.
Using two different cost metrics, expected running time and the area under
the empirical cumulative distribution function curve, we find that in several
cases the best configurations with respect to expected running time are
obtained when using the area under the empirical cumulative distribution
function curve as the cost metric during the configuration process. Our results
suggest that even when interested in expected running time performance, it
might be preferable to use anytime performance measures for the configuration
task. We also observe that tuning for expected running time is much more
sensitive with respect to the budget that is allocated to the target
algorithms
Benchmarking a Genetic Algorithm with Configurable Crossover Probability
We investigate a family of Genetic Algorithms (GAs) which
creates offspring either from mutation or by recombining two randomly chosen
parents. By scaling the crossover probability, we can thus interpolate from a
fully mutation-only algorithm towards a fully crossover-based GA. We analyze,
by empirical means, how the performance depends on the interplay of population
size and the crossover probability.
Our comparison on 25 pseudo-Boolean optimization problems reveals an
advantage of crossover-based configurations on several easy optimization tasks,
whereas the picture for more complex optimization problems is rather mixed.
Moreover, we observe that the ``fast'' mutation scheme with its are power-law
distributed mutation strengths outperforms standard bit mutation on complex
optimization tasks when it is combined with crossover, but performs worse in
the absence of crossover.
We then take a closer look at the surprisingly good performance of the
crossover-based GAs on the well-known LeadingOnes benchmark
problem. We observe that the optimal crossover probability increases with
increasing population size . At the same time, it decreases with
increasing problem dimension, indicating that the advantages of the crossover
are not visible in the asymptotic view classically applied in runtime analysis.
We therefore argue that a mathematical investigation for fixed dimensions might
help us observe effects which are not visible when focusing exclusively on
asymptotic performance bounds
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