From fitness landscape analysis to designing evolutionary algorithms: The case study in automatic generation of function block applications

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • St. Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO)
  • Luleå University of Technology


Search-based software engineering, a discipline that often requires finding optimal solutions, can be a viable source for problems that bridge theory and practice of evolutionary computation. In this research we consider one such problem: generation of data connections in a distributed control application designed according to the IEC 61499 industry standard. We perform the analysis of the fitness landscape of this problem and find why exactly the simplistic (1 + 1) evolutionary algorithm is slower than expected when finding an optimal solution to this problem. To counteract, we develop a population-based algorithm that explicitly maximises diversity among the individuals in the population. We show that this measure indeed helps to improve the running times.


Original languageEnglish
Title of host publicationProceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, GECCO 2018
Publication statusPublished - 6 Jul 2018
MoE publication typeA4 Article in a conference publication
EventGenetic and Evolutionary Computation Conference Companion - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018


ConferenceGenetic and Evolutionary Computation Conference Companion
Abbreviated titleGECCO
OtherA Recombination of the 27th International Conference on Genetic Algorithms (ICGA) and the 23rd Annual Genetic Programming Conference (GP)

    Research areas

  • Evolutionary computation, Population diversity, Program synthesis, Search-based software engineering

ID: 27984760