Sebanyak 641 item atau buku ditemukan

Understanding and using QBasic

... a Boolean expression (after George Boule, an English mathematician and
logician). All relational expressions are examples of Boolean expressions.
Relational expressions are formed using the six relational operators shown in
Table 3.1.

Understanding and using Microsoft Windows 95

Change the current country setting (English-United States) to Swedish. You will
have to scroll down the list to see this choice. After you restart your computer, you
will notice how the numeric formats change for date, time, and currency. Click on
 ...

Understanding and Using Microsoft Visual Basic 4.0

This text provides a complete reference to Microsoft Visual Basic Version 4. It teaches specific skills required to program in Visual Basic Version 4, presenting concepts and skills through guided activities, exercises, applications and examples.

Caption - LabelCap ' Read in the Italian pronouns and their English equivalents
Input #1, Prol, Pro2, Pro3, Pro4, Pro5, Pro6, Pro7, Pro8 Input #1, Engl, Eng2,
Eng3, Eng4, Eng5. Eng6, Eng7, Eng8 ' Set the captions for the 8 command
buttons ...

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

5th European Conference, EvoBIO 2007, Valencia, Spain, April 11-13, 2007, Proceedings

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences data in order to unravel the mysteries of biological function, leading to new drugs and therapies for human disease. Life sciences data come in the form of biological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c patterns present in a given dataset and then to interpret those patterns. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the Fifth European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2007) was to bring experts in computer science - gether with experts in bioinformatics and the biological sciences to explore new and novel methods for solving complex biological problems. The ?fth EvoBIO conference was held in Valencia, Spain during April 11-13, 2007 at the Universidad Politecnica de Valencia. EvioBIO 2007 was held jointly with the Tenth European Conference on Genetic Programming (EuroGP 2007), the Seventh European Conference on Evolutionary Computation in Combina- rial Optimisation (EvoCOP 2007), and the Evo Workshops. Collectively, the c- ferences and workshops are organized under the name Evo* (www. evostar. org).

... clinical safety (black), efficacy (red), formulation (green), PK/bioavailability (
blue), commercial (yellow), toxicology (gray), cost of goods (purple) and others (
white). development of computational tools applicable for pharmacokinetic
profiling, ...

Automatic Generation of Neural Network Architecture Using Evolutionary Computation

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.

7.2 Evolutionary Computing to Analyse a NN Although this combination of GAs
and NNs is not common, GAs can be used to analyse or explain neural networks.
In [13] GAs are used as a neural network inversion tool in that they can find the ...

Practical Applications of Evolutionary Computation to Financial Engineering

Robust Techniques for Forecasting, Trading and Hedging

“Practical Applications of Evolutionary Computation to Financial Engineering” presents the state of the art techniques in Financial Engineering using recent results in Machine Learning and Evolutionary Computation. This book bridges the gap between academics in computer science and traders and explains the basic ideas of the proposed systems and the financial problems in ways that can be understood by readers without previous knowledge on either of the fields. To cement the ideas discussed in the book, software packages are offered that implement the systems described within. The book is structured so that each chapter can be read independently from the others. Chapters 1 and 2 describe evolutionary computation. The third chapter is an introduction to financial engineering problems for readers who are unfamiliar with this area. The following chapters each deal, in turn, with a different problem in the financial engineering field describing each problem in detail and focusing on solutions based on evolutionary computation. Finally, the two appendixes describe software packages that implement the solutions discussed in this book, including installation manuals and parameter explanations.

To cement the ideas discussed in the book, software packages are offered that implement the systems described within. The book is structured so that each chapter can be read independently from the others.

Evolutionary Computation

Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.

Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving.

Sinkronisasi Program dan Pembiayaan Pembangunan Jangka Pendek 2018-2020 Keterpaduan Pengembangan Kawasan dengan Infrastruktur PUPR Pulau Sumatera

dipengaruhi oleh tingkat pendidikan dan kondisi pembangunan infrastruktur
yang tarus dilakukan oleh pemerintah dalam upaya meningkatkan daya saing.
Selanjutnya pembangunan manusia yang berkelanjutan akan berdampak pada
 ...

Sinkronisasi Program dan Pembiayaan Pembangunan Jangka Pendek 2018-2020 Keterpaduan Pengembangan Kawasan dengan Infrastruktur PUPR Pulau Jawa

Nilai tersebut dipengaruhi oleh tingkat pendidikan masyarakat DKI Jakarta yang
sebagian besar merupakan lulusan perguruan tinggi dan kejuruan. Hal ini tak
lepas dari upaya pemerintah daerah dalam mewujudkan sumber daya manusia
 ...