TAILIEUCHUNG - STOCHASTIC OPTIMIZATION SEEING THE OPTIMAL FOR THE UNCERTAIN

Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in “fighting uncertainty with uncertainty†. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields | STOCHASTIC OPTIMIZATION - SEEING THE OPTIMAL FOR THE UNCERTAIN Edited by Ioannis Dritsas Stochastic Optimization - Seeing the Optimal for the Uncertain Edited by loannis Dritsas Published by InTech Janeza Trdine 9 51000 Rijeka Croatia Copyright 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution license which permits to copy distribute transmit and adapt the work in any medium so long as the original work is properly cited. After this work has been published by InTech authors have the right to republish it in whole or part in any publication of which they are the author and to make other personal use of the work. Any republication referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials instructions methods or ideas contained in the book. Publishing Process Manager Ivana Lorkovic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright Kim D. French 2010. Used under license from First published February 2011 Printed in India A free online edition of this book is available at Additional hard copies can be obtained from orders@ Stochastic Optimization - Seeing the Optimal for the Uncertain Edited by Ioannis Dritsas p. cm. ISBN 978-953-307-829-8 I kfTC LI OPEN ACCESS INI Evn PUBLISHER INTECH open free online editions of InTech Books and Journals can be found at .

• Kỹ thuật lập trình
• Genetic Algorithm
• Stochastic Methods
• Generator Design
• Reluctance Machine
• Transmission Investments
• Noise Reduction
• Solving uncapacitated multiple allocation p hub center problem
• Dijkstra’s algorithm based genetic algorithm and simulated annealing
• Dijkstra’s algorithm based genetic algorithm
• Large scale standard data sets
• P hub center problem
• International Journal of computer science & communication networks
• Anomaly detection using genetic
• SVM algorithm in data mining
• Algorithm in data mining
• K mean algorithm
• Two algorithm genetic and SVM
• Performance analysis of a sugar plant using genetic algorithm
• Optimization of crystallization system of a sugar plant using genetic algorithm
• A sugar plant using genetic algorithm
• Refining system fails
• The crystallization system includes crystallization units
• International Journal of Computer Networks and Communications Security
• Hybrid of genetic algorithm
• Continuous ant colony optimization for optimum solution
• Combining genetic algorithm
• The new hybrid algorithm
• Journal of Computer Science and Cybernetics
• Improved genetic algorithm
• Test data generation for simulink models
• Conventional genetic algorithm
• Generate test data for Simulink models
• Vietnam Journal of Mechanics
• The genetic algorithm
• The consequential convex approximation programming
• Composite structure optimization
• Linear elastic fiber reinforced composite laminate
• Survey on cloud computing steganography using genetic algorithm
• Cloud computing steganography using genetic algorithm
• Cloud computing steganography
• Protect stored data
• Bi objective optimization
• Integrated forward/reverse logistic network
• Non dominated sorting genetic algorithm II
• Bi objective freight scheduling optimization
• Bài giảng Máy học nâng cao
• Máy học nâng cao
• Genetic algorithm operators and parameters Working mechanism
• Successive Zooming
• Network Operator
• Mathematical Equation
• Solving a multi objective location routing problem
• Infectious waste disposal
• Hybrid goal programming and hybrid genetic algorithm
• Hybrid goal programming
• Hybrid genetic algorithm
• Portfolio optimization
• Objective function
• Artificial intelligence methods
• Portfolio selection using genetic algorithm
• Evolutionary computation
• Fleet sizing problem
• Time windows using genetic algorithm
• Differential evolution algorithm
• Fuel reloading optimization
• Nuclear reactor
• Meta heuristic approaches
• A genetic algorithm for preemptive scheduling of a single machine
• Preemptive scheduling of a single machine
• Large scale problems
• A genetic algorithm
• Optimal Recombination Problem
• Boolean Linear Programming
• Optimal recombination
• BMC Bioinformatics
• Genetic code adaptability
• Evolutionary computing
• Fitness sharing
• Ensemble learning
• Co evolution algorithm
• Feature selection
• Ensemble learning algorithm to produce
• A new Approach to Improving Multilingual Summarization
• a Genetic Algorithm
• Marina Litvak Ben Gurion
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• TSP solver using genetic algorithm
• The outline of the GA for TSP
• Mutation by 2 opt
• Crossover operator
• Greedy Subtour Crossover
• Survivor Selection
• Other parameters
• Method for university course timetabling problems
• Application in Hanoi open university
• University course timetabling
• P hub maximal covering problem
• Binary coverage
• r allocation strategy
• DHL services
• Supply chains
• Possibility measures
• Time value of money
• Necessity measures
• Rented warehouse
• Piezoelectric laminated composite plate using genetic algorithm
• The generalized displacements
• Piezoelectric laminated composite plate
• The piezoelectric actuator efficiency
• Requirements based upon quality approach
• Interactive genetic algorithm focussed on agile methodologies
• The successive releases of the system