Evolutionary Algorithms in Engineering and Computer Science

Summary

Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyv䳫yl䬠Finland M. M. M䫥l䬠University of Jyv䳫yl䬠Finland P. Neittaanm䫩, University of Jyv䳫yl䬠Finland J. P鲩aux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.         

Table of Contents

METHODOLOGICAL ASPECTS.

Using Genetic Algorithms for Optimization: Technology Transfer in Action (J. Haataja).

An Introduction to Evolutionary Computation and Some Applications (D. Fogel).

Evolutionary Computation: Recent Developments and Open Issues (K. De Jong).

Some Recent Important Foundational Results in Evolutionary Computation (D. Fogel). Evolutionary Algorithms for Engineering Applications (Z. Michalewicz, et al.).

Embedded Path Tracing and Neighbourhood Search Techniques (C. Reeves T. Yamada). Parallel and Distributed Evolutionary Algorithms (M. Tomassini).

Evolutionary Multi-Criterion Optimization (K. Deb).

ACO Algorithms for the Traveling Salesman Problem (T. St?M. Dorigo).

Genetic Programming: Turing's Third Way to Achieve Machine Intelligence (J. Koza, et al.).

Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming (F. Bennett, et al.).

APPLICATION-ORIENTED APPROACHES.

Multidisciplinary Hybrid Constrained GA Optimization (G. Dulikravich, et al.).

Genetic Algorithm as a Tool for Solving Electrical Engineering Problems (M. Rudnicki, et al.).

Genetic Algorithms in Shape Optimization: Finite and Boundary Element Applications (M. Cerrolaza W. Annicchiarico).

Genetic Algorithms and Fractals (E. Lutton).

Three Evolutionary Approaches to Clustering (H. Luchian).

INDUSTRIAL APPLICATIONS.

Evolutionary Algorithms Applied to Academic and Industrial Test Cases (T. B䣫, et al.).

Optimization of an Active Noise Control System Inside an Aircraft, Based on the Simultaneous Optimal Positioning of Microphones and Speakers, with the Use of a Genetic Algorithm (Z. Diamantis, et al.).

Generator Scheduling in Power Systems by Genetic Algorithm and Expert System (B. Galvan, et al.).

Efficient Partitioning Methods for 3-D Unstructured Grids Using Genetic Algorithms (A. Giotis, et al.).

Genetic Algorithms in Shape Optimization of a Paper Machine Headbox (J. H䭤l䩮en, et al.).

A Parallel Genetic Algorithm for Multi-Objective Optimization in Computational Fluid Dynamics (N. Marco, et al.).

Application of a Multi Objective Genetic Algorithm and a Neural Network to the Optimisation of Foundry Processes (G. Meneghetti, et al.).

Circuit Partitioning Using Evolution Algorithms (J. Montiel-Nelson, et al.).