Miettinen K., Neittaanmäki P., Mäkelä M.M., Périaux J. (eds.) Evolutionary Algorithms in Engineering and Computer Science

John Wiley, 1999. — 500.This book collects the papers of the invited lecturers of EUROGEN99, the Short Course on Evolutionary Algorithms in Engineering and Computer Science, held at the University of Jyvaskyla, Finland, between May 30 and June 3, 1999. In addition, this book contains several industrial presentations given during the Short Course by contributors belonging to the European Thematic Network INGENET or other European projects.
EUROGEN99 is the third in this series of EUROGEN Short Courses. The first course took place in Las Palmas in 1995 and the second in Trieste in 1997. EUROGEN95 concentrated on the theory of genetic algorithms whereas EUROGEN97 was more focused on applications. The theme of EUROGEN99 was chosen to be Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications. Special emphasis was given to practical industrial and engineering applications.
Evolution algorithms are computer science-oriented techniques which imitate nature according to Darwin's principle of "survival of the fittest." A population of solutions is treated with genetic operations, namely selection, crossover and mutation in order to produce improved generations. This evolution approach can be applied in multidisciplinary areas. Among them are fluid dynamics, electrical engineering, engineering design, electromagnetic, scheduling, pattern recognition and signal processing. All these applications are considered in this volume together with theoretical and numerical aspects of evolutionary computation for global optimization problems. The papers aim at creating a bridge between artificial intelligence, scientific computing and engineering so that both the performance and the robustness of solution methods for solving industrial problems can be improved.Part I Methodological aspects
Using Genetic Algorithms for Optimization: Technology Transfer in Action
An Introduction to Evolutionary Computation and Some Applications
Evolutionary Computation: Recent Developments and Open Issues
Some Recent Important Foundational Results in Evolutionary Computation
Evolutionary Algorithms for Engineering Applications
Embedded Path Tracing and Neighbourhood Search Techniques
Parallel and Distributed Evolutionary Algorithms
Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design
ACO Algorithms for the "raveling Salesman Problem
Genetic Programming: firing's Third Way to Achieve Machine Intelligence
Automatic Synthesis of the Topology and Sizing for Analog Electrical Using Genetic Programming
Part II Application-oriented approaches
Multidisciplinary Hybrid Constrained GA Optimization
Genetic Algorithm as a Tool for Solving Electrical Engineering Problems
Genetic Algorithms in Shape Optimization: Finite and Boundary Element
Genetic Algorithms and Fractals
Three Evolutionary Approaches to Clustering
Part III Industrial Applications
Evolutionary Algorithms Applied to Academic and Industrial Test Cases
Optimization of an Active Noise Control System inside an Aircraft, Simultaneous Optimal Positioning of Microphones and Speakers, with a Genetic Algorithm
Generator Scheduling in Power Systems by Genetic Algorithm and Expert System
Efficient Partitioning Methods for 3-D Unstructured Grids Using Genetic Algorithms
Genetic Algorithms in Shape Optimization of a Paper Machine Headbox
A Parallel Genetic Algorithm for Multi-Objective Optimization in Computational Fluid Dynamics
Application of a Multi Objective Genetic Algorithm and a Neural Net Optimisation of Foundry Processes
Circuit Partitioning Using Evolution Algorithms