Emphasis on verb forms, complex structures, & grammar concepts using example & exercises to reinforce the development of speaking, listening, writing & reading abilities.
Emphasis on verb forms, complex structures, & grammar concepts using example & exercises to reinforce the development of speaking, listening, writing & reading abilities.
A classic developmental skills text for intermediate to advanced students of English, Understanding and Using English Grammar is a comprehensive reference grammar as well as a stimulating and teachable classroom text. While keeping the same basic approach and material as in earlier editions, the Fourth Edition more fully develops communicative and interactive language-learning activities. Some of the new features are: Innovative warm-up exercises that precede the grammar charts and introduce points to be taught Structure-based listening exercises ranging from casual speech to academic content Academic readings that highlight the targeted grammar structures Greatly expanded speaking practice with extensive pair, group and class work Corpus-informed syllabus that reflects the discourse patterns of spoken and written English Audio CDs and listening script in the back of the Student Book The program components include the Student Book (Full Edition and Volume A and Volume B), Workbook (Full Edition and Volume A and Volume B), Chartbook, Teacher's Guide, and Test Bank. Click on "Course-Specific Resources" on the left for more details. For an online workbook, see Understanding and Using English Grammar Interactive.
While keeping the same basic approach and material as in earlier editions, the Fourth Edition more fully develops communicative and interactive language-learning activities.
This study investigated the relationship between intellectual capital and public sector performance in Malaysia. Findings revealed that there is a significant and positive relationship between two, and one way of increasing the level of public sector performance is to tie performance to intellectual capital.
... to my incredible parents Zainab Abdul and Mat Sulihan, my beloved husband
Zainal Ahmad and my darling children Misha Zahra and Melissa Raudhah, as
well as to my supportive brother Zairi Kamaruddin and the rest of the family.
The Italian community in Artificial Life and Evolutionary computation has grown remarkably in recent years, and this book is the first broad collection of its major interests and achievements (including contributions from foreign countries). The contributions in Artificial Life as well as in Evolutionary Computation allow one to see the deep connections between the two fields. The topics addressed are extremely relevant for present day research in Artificial Life and in Evolutionary Computation, which include important contributions from very well-known researchers. The volume provides a very broad picture of the Italian activities in this field. Sample Chapter(s). Chapter 1: Cognitive Dynamics in an Automata Gas (906 KB). Contents: Diffusion of Shapes (R S Shaw & N H Packard); FDC-Based Particle Swarm Optimization (A Azzini et al.); An Evolutionary Predictive Approach to Design High Dimensional Experiments (D De March et al.); Prefrontal Cortex and Action Sequences: A Review on Neural Computational Models (I Gaudiello et al.); Bio-Inspired ICT for Evolutionary Emotional Intelligence (M Villamira & P Cipresso); Cooperation in Corvids: A Simulative Study with Evolved Robots (O Miglino et al.); Distributed Processes in a Agent-Based Model of Innovation (L Ansaloni et al.); Imaginary or Actual Artificial Worlds Using a New Tool in the ABM Perspective (P Terna); Dynamics of Interconnected Boolean Networks with Scale-Free Topology (C Damiani et al.); Semi-Synthetical Minimal Cells (P Stano & P L Luisi); and other papers. Readership: Graduate students, academics and researchers in the field of complex systems, artificial intelligence and robotics.
to Python and vice versa (via the Python-UNO bridge, incorporated in OOo); (iii)
do symbolic calculations in Python (via http://code.google.com/p/sympy/); and (iv)
use Social Network Analysis from Python, with tools like the Igraph library ...
This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.
—Ian Hacking In this chapter we discuss the role of experiments in evolutionary
computation. First, problems related to experiments are presented. Objections
stated by theoreticians, for example, “Algorithms are formal objects and should
be ...
Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.
This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general.
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
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.
