The Theory of Evolution StrategiesSpringer Science & Business Media, 27.03.2001 - 380 Seiten Evolutionary Algorithms, in particular Evolution Strategies, Genetic Algorithms, or Evolutionary Programming, have found wide acceptance as robust optimization algorithms in the last ten years. Compared with the broad propagation and the resulting practical prosperity in different scientific fields, the theory has not progressed as much. This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES). The main emphasis is on understanding the functioning of these probabilistic optimization algorithms in real-valued search spaces by investigating the dynamical properties of some well-established ES algorithms. The book introduces the basic concepts of this analysis, such as progress rate, quality gain, and self-adaptation response, and describes how to calculate these quantities. Based on the analysis, functioning principles are derived, aiming at a qualitative understanding of why and how ES algorithms work. |
Inhalt
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IX | 14 |
CVIII | 177 |
CIX | 178 |
CX | 181 |
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CXV | 188 |
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CC | 355 |
CCI | 356 |
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CCX | 363 |
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Häufige Begriffe und Wortgruppen
analysis analytical applied approximation asymptotic behavior Beyer calculation centroid components Computation condition considered const convergence curvature derivation descendant determined dominant recombination dynamics equation error term Evolution Strategies Evolutionary Evolutionary Algorithms expected value expressed fitness function fitness landscapes follows Gaussian Genetic Algorithms Hermite polynomials inserting integral investigated learning parameter linear log-normal operator maximal mean value measure mutation operators mutation strength normal distribution obtains offspring optimal optimum order statistic parameter space parental performance population probability density progress coefficient progress rate formula quality gain Qy(x random variable Rechenberg recombination residual distance result Schwefel Sect selection self-adaptation simulation sphere model standard deviation stationary statistical statistically independent strategy parameters substitution success probability surrogate mutations Taylor expansion Taylor series theoretical tion transition density two-point operator valid yields