Facilitating Evolutionary Algorithm Analysis with Persistent Data Structures

  • Erik PitzerEmail author
  • Michael Affenzeller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10671)


Evolutionary algorithm analysis is often impeded by the large amounts of intermediate data that is usually discarded and has to be painstakingly reconstructed for real-world large-scale applications. In the recent past persistent data structures have been developed which offer extremely compact storage with acceptable runtime penalties. In this work two promising persistent data structures are explored in the context of evolutionary computation with the hope to open the door to simplified analysis of large-scale evolutionary algorithm runs.



The work described in this paper was performed within the COMET Project Heuristic Optimization in Production and Logistics (HOPL), #843532 funded by the Austrian Research Promotion Agency (FFG).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department Software EngineeringUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler UniversityLinzAustria

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