Finding Good Affinity Patterns for Matchmaking Parties Assignment through Evolutionary Computation

  • Sho Kuroiwa
  • Keiichi Yasumoto
  • Yoshihiro Murata
  • Minoru Ito
Conference paper

DOI: 10.1007/978-3-642-32964-7_50

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)
Cite this paper as:
Kuroiwa S., Yasumoto K., Murata Y., Ito M. (2012) Finding Good Affinity Patterns for Matchmaking Parties Assignment through Evolutionary Computation. In: Coello C.A.C., Cutello V., Deb K., Forrest S., Nicosia G., Pavone M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg

Abstract

There is a demand to maximize the number of successful couples in matchmaking parties called “Gokon” in Japanese. In this paper, we propose a method to find good affinity patterns between men and women from resulting Gokon matches by encoding their attribute information into solutions and using an evolutionary computation scheme. We also propose a system to assign the best members to Gokons based on the method. To derive good affinity patterns, a specified number of solutions as chromosomes of evolutionary computation (EC) are initially prepared in the system. By feeding back the results of Gokon to the solutions as fitness value of EC, semi-optimal solutions are derived. To realize the proposed system, we need simultaneous search of multiple different good affinity patterns and efficient evaluation of solutions through as small number of Gokons as possible with various attribute members. To meet these challenges, we devise new methods for efficient selection operation inspired by Multi-niches Crowding method and reuse of past Gokon results to evaluate new solutions. To evaluate the system, we used the NMax problem assuming that there would be N good affinity patterns between men and women as a benchmark test. Through computer simulations for N = 12, we confirmed that the proposed system achieves almost twice as many good matches as a conventional method with about half the evaluation times.

Keywords

Evolutionary Computation Matchmaking Party Multi- niches Crowding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sho Kuroiwa
    • 1
    • 2
  • Keiichi Yasumoto
    • 1
  • Yoshihiro Murata
    • 3
  • Minoru Ito
    • 1
  1. 1.Nara Institute of Science and TechnologyNaraJapan
  2. 2.Hopeful Monster CorporationNaraJapan
  3. 3.Hiroshima City UniversityHiroshimaJapan

Personalised recommendations