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8. Evolution Strategies and Other Methods

8. Evolution Strategies and Other Methods. Evolution strategies (ES) algorithms imitating the principles of natural evolution as a method to solve parameter optimization problems developed in Germany during 1960s in 1963, two students at the Technical University of Berlin.

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8. Evolution Strategies and Other Methods

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  1. 8. Evolution Strategies and Other Methods • Evolution strategies (ES) • algorithms imitating the principles of natural evolution as a method to solve parameter optimization problems • developed in Germany during 1960s • in 1963, two students at the Technical University of Berlin

  2. 8.1 Evolution of evolution strategies • Earliest ES • population consisting of one individual only • only one genetic operator: mutation • individual • float-valued vector • v = (x, ) • x: a point in the search space • : a vector of standard deviation • mutation realized by replacing x by • xt+1 = xt + N(0,  ) • an offspring accepted iff it has a better fitness and all constraints are satisfied

  3. 8.1 Evolution of evolution strategies • An example • p.160 • Various modifications • Perform well in numerical domain • ES were dedicated to function optimization problem • an example of EP (Evolution Program) • using appropriate data structures (float vector) • genetic operators for the problem domain

  4. 8.2 Comparison of ES and GA • Domain • ES: for numerical optimization • GA: (general-purpose) adaptive search technique • Similarities • maintain population of potential solutions • selection principle of survival of the fitter individuals • Differences • representation • selection process (ES deterministic, GA random) • …..

  5. 8.3 Multimodal and multiobjective function optimization • Most chapters of this book • locating single, global optimum of a function • This section • multimodal optimization • several optima • multiobjective optimization • more than one criterion for optimization • VEGA program [339]

  6. 8.4 Other evolution programs • Classical GA • not appropriate tool for local fine tuning • less precise solution to numerical optimization problems than ES • Modifications of GA • Delta coding • Serial selection • Scatter/Tabu search

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