Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems

  • Piotr Faliszewski
  • Jakub Sawicki
  • Robert Schaefer
  • Maciej Smołka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)

Abstract

Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. In case in which the insensitivity of the fitness function is the main obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function, surrounding its minimizers. In this paper we suggest a way of maintaining diversity of the population in the plateau regions, based on a new approach for the selection based on the theory of multiwinner elections among autonomous agents. The paper delivers a detailed description of the new selection algorithm, computational experiments that guide the choice of the proper multiwinner rule to use, and a preliminary experiment showing the proposed algorithm’s effectiveness in exploring a fitness function’s plateau.

Keywords

Ill-posed global optimization problems New tournament-like selection Fitness insensitivity 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Piotr Faliszewski
    • 1
  • Jakub Sawicki
    • 1
  • Robert Schaefer
    • 1
  • Maciej Smołka
    • 1
  1. 1.AGH University of Science and TechnologyKrakówPoland

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