Stigmergic Optimization (studies In Computational Intelligence)

Biologists studied the behavior of social insects for a long time. After millions of years of evolution all these species have developed incredible solutions for a wide range of problems. The intelligent solutions to problems naturally emerge from the self-organization and indirect communication of these individuals. Indirect interactions occur between two individuals when one of them modifies the environment and the other responds to the new environment at a later time. Such an interaction is an example of ‘stigmergy’. This book deals with the application of stigmergy for a variety of optimization problems. This volume comprises 12 chapters including an introductory chapter giving the fundamental definitions, inspirations and some research challenges. Important features include a detailed overview of all the stigmergic optimization paradigms, excellent coverage of timely, advanced stigmergic optimization topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and application of stigmergic optimization will find the comprehensive coverage of this book invaluable. Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di?culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti?cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the?nal product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, arti?cial intelligence, system identi?cation, process monitoring, process modelling and optimal control. Both simulation and experimental validation are performed in this study to demonstrate the suitability and feasibility of proposed methodologies. An online biomass sensor is constructed using a - current neural network for predicting the biomass concentration online with only three measurements (dissolved oxygen, volume and feed rate). Results show that the proposed sensor is comparable or even superior to other sensors proposed in the literature that use more than three measurements. Biote- nological processes are modelled by cascading two recurrent neural networks. It is found that neural models are able to describe the processes with high accuracy. Optimization of the?nal product is achieved using modi?ed genetic algorithms to determine optimal feed rate pro?les. Experimental results of the corresponding production yields demonstrate that genetic algorithms are powerful tools for optimization of highly nonlinear systems. Moreover, a c- bination of recurrentneural networks and genetic algorithms provides a useful and cost-e?ective methodology for optimizing biotechnological processes. Stigmergic optimization : inspiration, technologies, and perspectives / Crina Grosan, Ajith Abraham Stigmergic autonomous navigation in collective robotics / Renato R. Cazangi, Fernando J. Von Zuben, Mauricio F. Figueiredo A general approach to swarm coordination using circle formation / Karthikeyan Swaminathan, Ali A. Minai Stigmergic navigation for multi-agent teams in complex environments / Alfred Wurr, John Anderson Physically realistic self-assembly simulation system / Vadim Gerasimov ... [et al.] Gliders and riders : a particle swarm selects for coherent space-time structures in evolving cellular automata / J.J. Ventrella Termite : a swarm intelligent routing algorithm for mobile wireless ad-hoc networks / Martin Roth, Stephen Wicker Stochastic diffusion search : partial function evaluation in swarm intelligence dynamic optimisation / Kris De Meyer, Slawomir J. Nasuto, Mark Bishop Linear multi-objective particle swarm optimization / Sanaz Mostaghim and Werner Halter and Anja Wille Cooperative particle swarm optimizers : a powerful and promising approach / Mohammed El-Abd, Mohamed Kamel Parallel particle swarm optimization algorithms with adaptive simulated annealing / Shu-Chuan Chu ... [et al.] Swarm intelligence : theoretical proof that empirical techniques are optimal / Dmitri Iourinski ... [et al.].

"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas.

biologists Studied The Behavior Of Social Insects For A Long Time. After Millions Of Years Of Evolution All These Species Have Developed Incredible Solutions For A Wide Range Of Problems. The Intelligent Solutions To Problems Naturally Emerge From The Self-organization And Indirect Communication Of These Individuals. Indirect Interactions Occur Between Two Individuals When One Of Them Modifies The Environment And The Other Responds To The New Environment At A Later Time. Such An Interaction Is An Example Of ‘stigmergy’. This Book Deals With The Application Of Stigmergy For A Variety Of Optimization Problems. This Volume Comprises 12 Chapters Including An Introductory Chapter Giving The Fundamental Definitions, Inspirations And Some Research Challenges.

important Features Include A Detailed Overview Of All The Stigmergic Optimization Paradigms, Excellent Coverage Of Timely, Advanced Stigmergic Optimization Topics, State-of-the-art Theoretical Research And Application Developments And Chapters Authored By Pioneers In The Field. Academics, Scientists As Well As Engineers Engaged In Research, Development And Application Of Stigmergic Optimization Will Find The Comprehensive Coverage Of This Book Invaluable.

Web Personalization can be defined as any set of actions that can tailor the Web experience to a particular user or set of users. To achieve effective personalization, organizations must rely on all available data, including the usage and click-stream data (reflecting user behaviour), the site content, the site structure, domain knowledge, as well as user demographics and profiles. In addition, efficient and intelligent techniques are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' Web experience. The aim of the International Workshop on Adaptive and Personalized Semantic Web that was held in the Sixteenth ACM Conference on Hypertext and Hypermedia (September 6-9, 2005, Salzburg, Austria) was to bring together researchers and practitioners in the fields of web engineering, adaptive hypermedia, semantic web technologies, knowledge management, information retrieval, user modelling, and other related disciplines which provide enabling technologies for personalization and adaptation on the World Wide Web. The book contains the papers presented during the workshop. Presentations of the papers are available online at (http://www.hci.gr) www.hci.gr .

web Personalization Can Be Defined As Any Set Of Actions That Can Tailor The Web Experience To A Particular User Or Set Of Users. To Achieve Effective Personalization, Organizations Must Rely On All Available Data, Including The Usage And Click-stream Data (reflecting User Behaviour), The Site Content, The Site Structure, Domain Knowledge, As Well As User Demographics And Profiles. In Addition, Efficient And Intelligent Techniques Are Needed To Mine This Data For Actionable Knowledge, And To Effectively Use The Discovered Knowledge To Enhance The Users' Web Experience. The Aim Of The International Workshop On Adaptive And Personalized Semantic Web That Was Held In The Sixteenth Acm Conference On Hypertext And Hypermedia (september 6-9, 2005, Salzburg, Austria) Was To Bring Together Researchers And Practitioners In The Fields Of Web Engineering, Adaptive Hypermedia, Semantic Web Technologies, Knowledge Management, Information Retrieval, User Modelling, And Other Related Disciplines Which Provide Enabling Technologies For Personalization And Adaptation On The World Wide Web. The Book Contains The Papers Presented During The Workshop. Presentations Of The Papers Are Available Online At www.hci.gr.

This book presents logical approaches to monitoring, modelling and optimization of fed-batch fermentation processes based on artificial intelligence methods, in particular, neural networks and genetic algorithms. Both computer simulation and experimental validation are demonstrated in this book. The approaches proposed in this book can be readily adopted for different processes and control schemes to achieve maximum productivity with minimum development and production costs. These approaches can eliminate the difficulties of having to specify completely the structures and parameters of highly nonlinear bioprocess models. The book begins with a historical introduction to the field of bioprocess control based on artificial intelligence approaches, followed by two chapters covering the optimization of fed-batch culture using genetic algorithms. Online biomass soft-sensors are constructed in Chapter 4 using recurrent neural networks. The bioprocess is then modelled in Chapter 5 by cascading two soft-sensor neural networks. Optimization and validation of the final product are detailed in Chapters 6 and 7. The general conclusions are drawn in Chapter 8 Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. 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. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation. This book presents logical approaches to monitoring, modelling and optimization of fed-batch fermentation processes based on artificial intelligence methods, in particular, neural networks and genetic algorithms. Both computer simulation and experimental validation are demonstrated in this book. The approaches proposed in this book can be readily adopted for different processes and control schemes to achieve maximum productivity with minimum development and production costs. The book begins with a historical introduction to the field of bioprocesses control based on artificial intelligence approaches, followed by two chapters which cover the optimization of fed-batch culture using genetic algorithms. Online biomass soft-sensors are constructed in Chapter 4 using recurrent neural networks. The bioprocess is then modelled in Chapter 5 by cascading two soft-sensor neural networks. Optimization and validation of the final product are detailed in Chapters 6 and 7. The general conclusions are drawn in Chapter 8. -- from back cover "Kernel Based Algorithms for Mining Huge Data Sets is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book focuses on a broad range of machine learning algorithms in bioinformatics (gene microarrays), text categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas."--Jacket Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems. First studied in social insects like ants, indirect self-organizing interactions - known as "stigmergy" - occur when one individual modifies the environment and another subsequently responds to the new environment. The implications of self-organizing behavior extend to robotics and beyond. This book explores the application of stigmergy for a variety of optimization problems. The volume comprises 12 chapters including an introductory chapter conveying the fundamental definitions, inspirations and research challenges. "Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. 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. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation."--Jacket Web personalization can be defined as any set of actions that can tailor the Web experience to a particular user or set of users. This book talks about effective personalization, the usage and click-stream data (reflecting user behaviour), the site content, the site structure, domain knowledge, as well as user demographics and profiles Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. Willian C. Jason DSU Title III 2007-2012 funds