A Brief Introduction to Evolutionary Algorithms from the Perspective of Management Science

  • Volker Nissen


Evolutionary Algorithms (EA) represent, as nature-inspired metaheuristics, a branch of computational intelligence or soft computing. Their working is based on a rough abstraction of the mechanisms of natural evolution. They imitate biological principles, such as a population-based approach, the inheritance of information, the variation of solutions through crossover and mutation, and the selection of individual solutions for reproduction based on fitness. Different variants of Evolutionary Algorithms (EA) exist, such as Genetic Algorithms, Evolution Strategies, and Genetic Programming.

This chapter presents a brief introduction to the basic forms of EA, their benefits and limitations, as well as the general procedure to develop and apply them in practice, including self-adaptation and hybridization. Then, computational frameworks that ease the development process as well as the integration of EA in modern business software are highlighted. Providers of optimization software such as MathWorks with the “Global Optimization Toolbox”, but also large players in enterprise resource planning (ERP), such as SAP SE with “SAP APO”, integrated EA in their canons of software-based optimization methods. However, following the “No-Free-Lunch Theorem”, metaheuristics such as EA always require a certain degree of adaptation to the individual problem at hand to provide good solutions. In practice, this can pose a problem for the unexperienced user of EA-based software products. The chapter concludes with a brief assessment of the current situation concerning EA from an application-oriented point of view.


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Authors and Affiliations

  • Volker Nissen
    • 1
  1. 1.Information Systems Engineering in ServicesUniversity of Technology IlmenauIlmenauGermany

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