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Linear Genetic Programming
Linear Genetic Programming examines the evolution of imperative computer programs written as linear sequences of instructions. In contrast to functional expressions or syntax trees used in traditional Genetic Programming (GP), Linear Genetic Programming (LGP) employs a linear program structure as genetic material whose primary characteristics are exploited to achieve acceleration of both execution time and evolutionary progress.
Classification and Modeling with Linguistic Information Granules : Advanced Approaches to Linguistic Data Mining
Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and modeling, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Biologically Inspired Algorithms for Financial Modelling
Then Part I provides a thorough guide to the various bioinspired methodologies – neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures.
An Introduction to Knowledge Engineering
Knowledge Engineering refers to the development of systems that use knowledge, rather than data, to solve many novel computing problems. This is achieved by the application of computing techniques, closely associated with human cognitive processes, for transforming data into knowledge. An Introduction to Knowledge Engineering presents a simple but detailed exploration of current and established work in the field.
Advances in Metaheuristics for Hard Optimization
The book gathers contributions related to the following topics: theoretical developments in metaheuristics; adaptation of discrete metaheuristics to continuous optimization; performance comparisons of metaheuristics; cooperative methods combining different approaches; parallel and distributed metaheuristics for multiobjective optimization; software implementations; and real-world applications.
3D Imaging for Safety and Security
This book is so far the first that covers the current state of the art in 3D imaging for safety and security. Special attention was given to advanced 3D imaging technologies in the context of safety and security applications. Comparative evaluation studies showing advantages of 3D imaging over traditional 2D imaging for a given computer vision or pattern recognition task were emphasized. Moreover, additional experts in the field of 3D imaging for safety and security were invited by the editors for a contribution to this book.
Complex Scheduling
This book deals with such complex scheduling problems and methods to solve them. It consists of three parts: The ?rst part (Chapters 1 and 2) contains a description of basic scheduling models with applications and an introduction into discrete optimization (covering complexity, shortest path algorithms, linear programming, network ?ow algorithms and general optimization methods). In the second part (Chapter 3) resource-constrained project scheduling problems are considered. Especially, methods like constraint propagation, branch-a- bound algorithms and heuristic procedures are described. Furthermore, lower bounds and general objective functions are discussed.
Cellular Genetic Algorithms
CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications.
Linkage in Evolutionary Computation
The whole volume consisting of 19 chapters is divided into 3 parts: Models and Theories; Operators and Frameworks; Applications. This edited volume will serve as a useful guide and reference for researchers who are currently working in the area of linkage. For postgraduate research students, this volume will serve as a good source of reference. It is also suitable as a text for a graduate level course focusing on linkage issues.
Knowledge-Driven Computing : Knowledge Engineering and Intelligent Computations
Knowledge-Driven Computing constitutes an emerging area of intensive research located at the intersection of Computational Intelligence and Knowledge Engineering with strong mathematical foundations. It embraces methods and approaches coming from diverse computational paradigms, such as evolutionary computation and nature-inspired algorithms, logic programming and constraint programming, rule-based systems, fuzzy sets and many others. The use of various knowledge representation formalisms and knowledge processing and computing paradigms is oriented towards the efficient resolution of computationally complex and difficult problems.
Biomimicry for Optimization, Control, and Automation
In this book, we focus onhowtousebiomimicryof the functionaloperationofthe “hardwareandso- ware” of biological systems for the development of optimization algorithms and feedbackcontrolsystemsthatextendourcapabilitiestoimplementsophisticated levels of automation. The primary focus is not on the modeling, emulation, or analysis of some biological system. The focus is on using “bio-inspiration” to inject new ideas, techniques, and perspective into the engineering of complex automation systems. There are many biological processes that, at some level of abstraction, can berepresentedasoptimizationprocesses,manyofwhichhaveasa basicpurpose automatic control, decision making, or automation.
Autonomous Robots and Agents
This book deals with the theoretical and methodological aspects of incorporating intelligence in Autonomous Robots and Agents. Challenges faced in the real world to accomplish complex tasks, which require collaborative efforts, and methods to overcome them, are detailed. Several informative articles deal with navigation, localization and mapping of mobile robots, a problem that engineers and researchers are grappling with all the time.This edited volume is targeted to present the latest state-of-the-art methodologies in Robotics. It is a compilation of the extended versions of the very best papers selected from the many that were presented at the 3rd International Conference on Autonomous Robots and Agents (ICARA 2006) which was held at Palmerston North, New Zealand from 11-14 December, 2006. Scientists and engineers who work with mobile robots will find this book very useful and stimulating.
Assembly Line Design : The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms
Efficient assembly line design is a problem of considerable industrial importance. Assembly Line Design will be bought by technical personnel working in design, planning and production departments in industry as well as managers in industry who want to learn more about concurrent engineering.
Artificial intelligence techniques for computer graphics
This volume contains both invited and selected extended papers from the last 3IA Conference (3IA’2008), together with an introduction presenting the area of Intelligent Computer Graphics and various Computer Graphics areas where introduction of intelligent techniques permitted to resolve important problems.
Applied Mathematics for Restructured Electric Power Systems : Optimization, Control, and Computational Intellige
Discusses the use of applied mathematics to solve challenging power system problems. This book covers such areas as: control, optimization, and computational intelligence. It follows a three-part format: a description of an important power system problem or problems; the practice and/or particular research approaches; and, research directions.
Applications of computational intelligence in biology : Current trends and open problems
The purpose of this book is to provide a medium for such an exchange of expertise and concerns. In order to achieve the goal, we have solicited cont- butions from both computational intelligence as well as biology researchers.
Analysis and Design of Intelligent Systems Using Soft Computing Techniques
This book comprises a selection of papers from IFSA 2007 on new methods for analysis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas.
Advances in Evolutionary Computing for System Design
Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book includes thirteen chapters covering a wide area of topics in evolutionary computing and applications including: -Introduction to evolutionary computing in system design - Evolutionary neuro-fuzzy systems - Evolution of fuzzy controllers - Genetic algorithms for multi-classifier design -Evolutionary grooming of traffic -Evolutionary particle swarms -Fuzzy logic systems using genetic algorithms - Evolutionary algorithms and immune learning for neural network-based controller design - Distributed problem solving using evolutionary learning -Evolutionary computing within grid environment -Evolutionary game theory in wireless mesh networks - Hybrid multiobjective evolutionary algorithms for the sailor assignment problem - Evolutionary techniques in hardware optimization
Advances in Evolutionary Algorithms : Theory, Design and Practice
The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated: Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines. Demonstrating the practical use of the suggested road map. Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications. Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain. Opening an important track for multiobjective GEA research that relies on decomposition principle.
Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management
Presents a careful selection of relevant applications of CI methods for transport, logistics, and supply chain management problems. The chapters illustrate the current state-of-the-art in the application of CI methods in these fields and should help and inspire researchers and practitioners to apply and develop efficient methods.
