Genetic algorithms in engineering and computer science
Genetic Algorithms in Engineering and Computer Science Edited by G. Winter University of Las Palmas, Canary Islands, Spain J. Périaux Dassault Aviation, Saint Cloud, France M. Galán P. Cuesta University of Las Palmas, Canary Islands, Spain This attractive book alerts us to the existence of evolution based software - Genetic Algorithms and Evolution Strategies-used for the study of complex systems and difficult optimization problems unresolved until now. Evolution algorithms are artificial intelligence techniques which mimic nature according to the 'survival of the fittest' (Darwin's principle). They randomly encode physical (quantitative or qualitative) variables via digital DNA inside computers and are known for their robustness to better explore large search spaces and find near-global optima than traditional optimization methods. The objectives of this volume are two-fold:to present a compendium of state-of-the-art lectures delivered by recognized experts in the field on theoretical, numerical and applied aspects of Genetic Algorithms for the computational treatment of continuous, discrete and combinatorial optimization problems.to provide a bridge between Artificial Intelligence and Scientific Computing in order to increase the performance of evolution programs for solving real life problems.Fluid dynamics, structure mechanics, electromagnetics, automation control, resource optimization, image processing and economics are the featured multi-disciplinary areas among others in Engineering and Applied Sciences where evolution works impressively well. This volume is aimed at graduate students, applied mathematicians, computer scientists, researchers and engineers who face challenging design optimization problems in Industry. They will enjoy implementing new programs using these evolution techniques which have been experimented with by Nature for 3.5 billion years.
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Evolving MultiAgent Systems
The Existential Pleasures of Genetic Algorithms
A General Study on Genetic Fuzzy Systems
11 other sections not shown
adaptive aerodynamic airfoil analysis antenna application approach Artificial associated behavior binary bits chromosome classifier systems coding complex control system convergence cost function crossover crossover operators defined defuzzification deterministic discrete domain electromagnetic elements encoding engineering equations evaluation evolution strategy evolutionary algorithms Evolutionary Computation Figure finite fitness function frequency fuzzy control fuzzy logic fuzzy logic controllers fuzzy models fuzzy rules fuzzy sets Fuzzy Systems gene genetic algorithms genetic operators genotype given global Goldberg gradient IEEE implementation individuals input International Conference learning linear linguistic load balancing matrix membership functions mesh method module Morgan Kaufmann multiple mutation operator neural networks neurons niches node objective function obtained offspring optimisation optimization problems parallel Parallel Computing parameters parent partition performance phenotype Proc procedure processor recombination operators representation represented scheme Schwefel search space selection shape simulation solution solve solver stochastic string structure techniques theorem vector Whitley