Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review

Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)

Abstract

Solving a dynamic optimization problem means that the obtained results depend explicitly on time as a parameter. There are two major branches in which dynamic optimization occurs: (i) in dynamic programming and optimal control, and (ii) in dynamic fitness landscapes and evolutionary computation. In both fields, solving such problems is established practice while at the same time special and advanced aspects are still subject of research. In this chapter, we intend to give a comparative study of the two branches of dynamic optimization. We review both problem settings, define them, and discuss approaches for and issues in solving them. The main focus here is to highlight the connections and parallels. In particular, we show that optimal control problems can be understood as dynamic fitness landscapes, where for linear systems this relationship can even be expressed analytically.

Keywords

Search Space Optimal Control Problem Evolutionary Computation Evolutionary Approach Mass Extinction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Information Technology, Department of Measurement Technology and Control EngineeringHTWK Leipzig University of Applied SciencesLeipzigGermany
  2. 2.Department of Information Systems and ComputingBrunel UniversityUxbridgeUnited Kingdom

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