壓縮包中有5篇論文，分別為《Data-driven analysis of variables and dependencies in continuous OPTIMIZATION problems and EDAs》這是一篇博士論文，較為詳細的介紹了各種EDA算法；《Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm》《Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering》《Supplementary material for Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二階混合矩的高斯分布估計算法》介紹了一些基于EDA的創新算法。
Evolutionary Computation (EC) deals with problem solving, OPTIMIZATION, and
machine learning techniques inspired by principles of natural evolution and ge-
netics. Just from this basic definition, it is clear that one of the main features of
the research community involved in the study of its theory and in its applications
is multidisciplinarity. For this reason, EC has been able to draw the attention of
an ever-increasing number of researchers and practitioners in several fields.
When joining Siemens in 2001, I also extended my research interest towards radio net-
work planning methodologies. This area of research brought together my personal interest
in mobile communications and in the design of efficient algorithms and data structures.
Between 2001 and 2003, I participated in the EU project Momentum, which was target-
ing the performance evaluation and OPTIMIZATION of UMTS radio networks. I
The recent developments in full duplex (FD) commu-
nication promise doubling the capacity of cellular networks using
self interference cancellation (SIC) techniques. FD small cells
with device-to-device (D2D) communication links could achieve
the expected capacity of the future cellular networks (5G). In
this work, we consider joint scheduling and dynamic power
algorithm (DPA) for a single cell FD small cell network with
D2D links (D2DLs). We formulate the optimal user selection and
power control as a non-linear programming (NLP) OPTIMIZATION
problem to get the optimal user scheduling and transmission
power in a given TTI. Our numerical results show that using
DPA gives better overall throughput performance than full power
transmission algorithm (FPA). Also, simultaneous transmissions
(combination of uplink (UL), downlink (DL), and D2D occur
80% of the time thereby increasing the spectral efficiency and
network capacity
This paper reviews key factors to practical ESD
protection design for RF and analog/mixed-signal (AMS) ICs,
including general challenges emerging, ESD-RFIC interactions,
RF ESD design OPTIMIZATION and prediction, RF ESD design
characterization, ESD-RFIC co-design technique, etc. Practical
design examples are discussed. It means to provide a systematic
and practical design flow for whole-chip ESD protection design
OPTIMIZATION and prediction for RF/AMS ICs to ensure 1 st Si
design success.
Why did an electricity market emerge? How does it really work? What are the perfor-
mance measures that we can use to tell that the electricity market under consideration
is well functioning? These are the questions that will be explored in this book. The
main purpose of this book is to introduce the fundamental theories and concepts that
underpintheelectricitymarketswhicharebasedonthreemajordisciplines:electrical
power engineering, economics, and OPTIMIZATION methods.
The basic topic of this book is solving problems from system and control theory using
convex OPTIMIZATION. We show that a wide variety of problems arising in system
and control theory can be reduced to a handful of standard convex and quasiconvex
OPTIMIZATION problems that involve matrix inequalities. For a few special cases there
are “analytic solutions” to these problems, but our main point is that they can be
solved numerically in all cases. These standard problems can be solved in polynomial-
time (by, e.g., the ellipsoid algorithm of Shor, Nemirovskii, and Yudin), and so are
tractable, at least in a theoretical sense. Recently developed interior-point methods
for these standard problems have been found to be extremely efficient in practice.
Therefore, we consider the original problems from system and control theory as solved.
n recent years, there have been many books published on power system OPTIMIZATION.
Most of these books do not cover applications of artifi cial intelligence based methods.
Moreover, with the recent increase of artifi cial intelligence applications in various fi elds,
it is becoming a new trend in solving OPTIMIZATION problems in engineering in general
due to its advantages of being simple and effi cient in tackling complex problems. For this
reason, the application of artifi cial intelligence in power systems has attracted the interest
of many researchers around the world during the last two decades. This book is a result
of our effort to provide information on the latest applications of artifi cial intelligence
to OPTIMIZATION problems in power systems before and after deregulation.
