A diversity maintaining population-based incremental learning algorithm

Mario Ventresca, Hamid R. Tizhoosh

Research output: Contribution to journalArticlepeer-review


In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning.

Original languageEnglish (US)
Pages (from-to)4038-4056
Number of pages19
JournalInformation Sciences
Issue number21
StatePublished - Nov 1 2008


  • Diversity control
  • Diversity maintenance
  • Opposition-based computing
  • Population-based incremental learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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