The Basics of Evolution Strategies: The Implementation of the Biomimetic Optimization Method in Educational Modules

Author:

Speck Olga12ORCID,Speck Thomas12ORCID,Baur Sabine2,Herdy Michael3

Affiliation:

1. Cluster of Excellence livMatS @ FIT—Freiburg Center for Interactive Materials and Bioinspired Technologies, University of Freiburg, Georges-Köhler-Allee 105, 79110 Freiburg, Germany

2. Plant Biomechanics Group @ Botanic Garden Freiburg, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany

3. Ingenieurbüro Herdy (IBH), Kaiserdamm 4, 14057 Berlin, Germany

Abstract

With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic evolutionary optimization procedure of evolution strategies. We provide three educational modules in work sheets that can be used by teachers and students to improve their understanding of evolution strategies. The educational module “Optimization of a Milk Carton” shows that the material consumption in producing a milk carton can be minimized using an evolution strategy with a mutative step size control. The use of a standard dice and a pocket calculator enables new milk cartons to be generated, with the offspring having the lowest material consumption becoming the parent of the next generation. The other educational modules deal with the so-called brachistochrone problem. The module “Fastest and Shortest Marble Track” provides a construction plan for a marble track whereby students can experimentally compare the “path of shortest length” with the “path of shortest time”. The EvoBrach software, is used in the module “Various Marble Track Shapes” to compare the running times of a marble on a straight line, a parabola, and a brachistochrone. In conclusion, the introduction to the biomimetic method of evolution strategies and the educational modules should deepen the understanding of both optimization problems and biological evolution.

Funder

DFG, German Research Foundation

Publisher

MDPI AG

Reference57 articles.

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