Evolutionary algorithms

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In computer science, evolution strategy (ES, from German Evolutionsstrategie) is an optimization technique based on ideas of adaptation and evolution. It belongs to a more general class of evolutionary computation.

Evolution strategies primarily use real-vector coding, and mutation, recombination, and environmental selection as its search operators. As common with evolutionary algorithms, the operators are applied in order: Mating selection, recombination, mutation, fitness function evaluation, and environmental selection. Performing the loop one time is called a generation, and this is continued until a termination criterion is met.

The first ES variants were not population based, but memorized only one search point (the parent) and one ((1+1)-ES) or more offspring (1+,lambda)-ES) at a time. Contemporary versions usually employ a population ((mu+,lambda)-ES) and are thus believed to be less prone to get stuck in local optima.

Mutation is performed by adding a gaussian distributed random value simultaneously to each vector element. The step size or mutation strength (ie. the standard deviation of this distribution) is usually learned during the optimization. This process is called self-adaptation, and it should keep the evolutionary process within the evolution window.

See also

• Bionics

References

• H.-G. Beyer and H.-P. Schwefel. Evolution Strategies: A Comprehensive Introduction. Journal Natural Computing, 1(1):3-52, 2002.
• Hans-Georg Beyer: The Theory of Evolution Strategies: Springer April 27, 2001.
• Hans-Paul Schwefel: Evolution and Optimum Seeking: New York: Wiley & Sons 1995.
• Ingo Rechenberg: Evolutionsstrategie '94. Stuttgart: Frommann-Holzboog 1994.
• J. Klockgether and H. P. Schwefel (1970). Two-Phase Nozzle And Hollow Core Jet Experiments. AEG-Forschungsinstitut. MDH Staustrahlrohr Project Group. Berlin, Federal Republic of Germany. Proceedings of the 11th Symposium on Engineering Aspects of Magneto-Hydrodynamics, Caltech, Pasadena, Cal., 24.-26.3. 1970.

Research Centers

• Bionics & Evolutiontechnique at the Technical University Berlin
• Chair of Systems Analysis (Ls11) - University of Dortmund
• Collaborative Research Center 531 - University of Dormund

External links

• Animation: Optimisation of a Two-Phase Flashing Nozzle with an Evolution Strategy. - Animation of the Classical Experimental Optmization of a two phase flashing nozzle made by Professor Hans-Paul Schwefel and J. Klockgether. The result was shown at the Proceedings of the 11th Symposium on Engineering Aspects of Magneto-Hydrodynamics, Caltech, Pasadena, Cal., 24.-26.3. 1970.
• Bionics – Building on Bio-Evolution. By Ingo Rechenberg - A Brief Tutorial.
• Comparison of Evolutionary Algorithms on a Benchmark Function Set - The 2005 IEEE Congress on Evolutionary Computation: Session on Real-Parameter Optimization - The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) applied in a benchmark function set.
• Evolution Strategies Animations - Some interesting animations and real world problems (such as format of lenses, bridges configurations, etc) solved through Evolution Strategies.
• Evolution Strategy in Action - 10 ES-Demonstrations. By Michael Herdy and Gianino Patone - 10 problems solved through Evolution Strategies.
• Evolutionary Algorithms Demos - There are some applets with Evolution Strategies and Genetic Algorithms that the user can manipulate to solve problems. Very interesting for a comparison between the two Evolutionary Algorithms.
• Evolutionary Car Racing Videos - The application of Evolution Strategies to evolve cars' behaviours.
• EvoWeb. - The European Network of Excellence in Evolutionary Computing.
• Learning To Fly: Evolving Helicopter Flight Through Simulated Evolution - A (10+23)-ES applied to evolve a helicopter flight controller.
• Professor Hans-Paul Schwefel talks to EvoNews - An interview with Professor Hans-Paul Schwefel, one of the Evolution Strategy pioneers.

de:Evolutionsstrategie es:Estrategia evolutiva