8,800 research outputs found
Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması
Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management. This model seeks an efficient frontier with the best trade-offs between two conflicting objectives of maximizing return and minimizing risk. The problem of determining an efficient frontier is known to be NP-hard. Due to the complexity of the problem, genetic algorithms have been widely employed by a growing number of researchers to solve this problem. In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature. Main specifications of the problems studied and the specifications of suggested genetic algorithms have been summarized
Clafer: Lightweight Modeling of Structure, Behaviour, and Variability
Embedded software is growing fast in size and complexity, leading to intimate
mixture of complex architectures and complex control. Consequently, software
specification requires modeling both structures and behaviour of systems.
Unfortunately, existing languages do not integrate these aspects well, usually
prioritizing one of them. It is common to develop a separate language for each
of these facets. In this paper, we contribute Clafer: a small language that
attempts to tackle this challenge. It combines rich structural modeling with
state of the art behavioural formalisms. We are not aware of any other modeling
language that seamlessly combines these facets common to system and software
modeling. We show how Clafer, in a single unified syntax and semantics, allows
capturing feature models (variability), component models, discrete control
models (automata) and variability encompassing all these aspects. The language
is built on top of first order logic with quantifiers over basic entities (for
modeling structures) combined with linear temporal logic (for modeling
behaviour). On top of this semantic foundation we build a simple but expressive
syntax, enriched with carefully selected syntactic expansions that cover
hierarchical modeling, associations, automata, scenarios, and Dwyer's property
patterns. We evaluate Clafer using a power window case study, and comparing it
against other notations that substantially overlap with its scope (SysML, AADL,
Temporal OCL and Live Sequence Charts), discussing benefits and perils of using
a single notation for the purpose
Best Subset Selection via a Modern Optimization Lens
In the last twenty-five years (1990-2014), algorithmic advances in integer
optimization combined with hardware improvements have resulted in an
astonishing 200 billion factor speedup in solving Mixed Integer Optimization
(MIO) problems. We present a MIO approach for solving the classical best subset
selection problem of choosing out of features in linear regression
given observations. We develop a discrete extension of modern first order
continuous optimization methods to find high quality feasible solutions that we
use as warm starts to a MIO solver that finds provably optimal solutions. The
resulting algorithm (a) provides a solution with a guarantee on its
suboptimality even if we terminate the algorithm early, (b) can accommodate
side constraints on the coefficients of the linear regression and (c) extends
to finding best subset solutions for the least absolute deviation loss
function. Using a wide variety of synthetic and real datasets, we demonstrate
that our approach solves problems with in the 1000s and in the 100s in
minutes to provable optimality, and finds near optimal solutions for in the
100s and in the 1000s in minutes. We also establish via numerical
experiments that the MIO approach performs better than {\texttt {Lasso}} and
other popularly used sparse learning procedures, in terms of achieving sparse
solutions with good predictive power.Comment: This is a revised version (May, 2015) of the first submission in June
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Verification of Agent-Based Artifact Systems
Artifact systems are a novel paradigm for specifying and implementing
business processes described in terms of interacting modules called artifacts.
Artifacts consist of data and lifecycles, accounting respectively for the
relational structure of the artifacts' states and their possible evolutions
over time. In this paper we put forward artifact-centric multi-agent systems, a
novel formalisation of artifact systems in the context of multi-agent systems
operating on them. Differently from the usual process-based models of services,
the semantics we give explicitly accounts for the data structures on which
artifact systems are defined. We study the model checking problem for
artifact-centric multi-agent systems against specifications written in a
quantified version of temporal-epistemic logic expressing the knowledge of the
agents in the exchange. We begin by noting that the problem is undecidable in
general. We then identify two noteworthy restrictions, one syntactical and one
semantical, that enable us to find bisimilar finite abstractions and therefore
reduce the model checking problem to the instance on finite models. Under these
assumptions we show that the model checking problem for these systems is
EXPSPACE-complete. We then introduce artifact-centric programs, compact and
declarative representations of the programs governing both the artifact system
and the agents. We show that, while these in principle generate infinite-state
systems, under natural conditions their verification problem can be solved on
finite abstractions that can be effectively computed from the programs. Finally
we exemplify the theoretical results of the paper through a mainstream
procurement scenario from the artifact systems literature
Tree rules in probabilistic transition system specifications with negative and quantitative premises
Probabilistic transition system specifications (PTSSs) in the ntmufnu/ntmuxnu
format provide structural operational semantics for Segala-type systems that
exhibit both probabilistic and nondeterministic behavior and guarantee that
isimilarity is a congruence.Similar to the nondeterministic case of rule format
tyft/tyxt, we show that the well-foundedness requirement is unnecessary in the
probabilistic setting. To achieve this, we first define an extended version of
the ntmufnu/ntmuxnu format in which quantitative premises and conclusions
include nested convex combinations of distributions. This format also
guarantees that bisimilarity is a congruence. Then, for a given (possibly
non-well-founded) PTSS in the new format, we construct an equivalent
well-founded transition system consisting of only rules of the simpler
(well-founded) probabilistic ntree format. Furthermore, we develop a
proof-theoretic notion for these PTSSs that coincides with the existing
stratification-based meaning in case the PTSS is stratifiable. This continues
the line of research lifting structural operational semantic results from the
nondeterministic setting to systems with both probabilistic and
nondeterministic behavior.Comment: In Proceedings EXPRESS/SOS 2012, arXiv:1208.244
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