TY  - BOOK
AU  - Levitin,Gregory
ED  - SpringerLink (Online service)
TI  - Computational Intelligence in Reliability Engineering: Evolutionary Techniques in Reliability Analysis and Optimization 
T2  - Studies in Computational Intelligence,
SN  - 9783540373681
AV  - TA329-348 
U1  - 519 23
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Mathematics
KW  - Engineering mathematics
KW  - System safety
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
KW  - Quality Control, Reliability, Safety and Risk
KW  - Applications of Mathematics
KW  - Computational Intelligence
N1  - Recent Advances in Optimal Reliability Allocation -- Multiobjective Metaheuristic Approaches to Reliability Optimization -- Genetic Algorithm Applications in Surveillance and Maintenance Optimization -- Genetic Algorithms and Monte Carlo Simulation for the Optimization of System Design and Operation -- New Evolutionary Methodologies for Integrated Safety System Design and Maintenance Optimization -- Optimal Redundancy Allocation of Multi-State Systems with Genetic Algorithms -- Intelligent Interactive Multiobjective Optimization of System Reliability -- Reliability Assessment of Composite Power Systems Using Genetic Algorithms -- Genetic Optimization of Multidimensional Technological Process Reliability -- Scheduling Multiple-version Programs on Multiple Processors -- Redundancy Optimization Problems with Uncertain Lifetimes -- Computational Intelligence Methods in Software Reliability Prediction
N2  - This book covers the recent applications of computational intelligence techniques in reliability engineering. This volume contains a survey of the contributions made to the optimal reliability design literature in the resent years and chapters devoted to different applications of a genetic algorithm in reliability engineering and to combinations of this algorithm with other computational intelligence techniques. Genetic algorithms are one of the most widely used metaheuristics, inspired by the optimization procedure that exists in nature, the biological phenomenon of evolution
UR  - http://dx.doi.org/10.1007/978-3-540-37368-1
ER  -