Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices

Wael Almadhoun, Mohammad Hamdan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In agile software processes, the issue of team size is an important one. In this work we look at how to find the optimal, or near optimal, self-organizing team size using a genetic algorithm (GA) which considers team communication efforts. Communication, authority, roles, and learning are the team's performance characteristics. The GA has been developed according to performance characteristics. A survey was used to evaluate the communication weight factors, which were qualitatively assessed and used in the algorithm's objective function. The GA experiments were performed in different stages: each stage results were tested and compared with the previous results. The results show that self-organizing teams of sizes ranged from five to nine members scored more. The model can be improved by adding other team characteristics, i.e. software development efforts and costs.

Original languageEnglish
Pages (from-to)1151-1165
Number of pages15
JournalJournal of Intelligent Systems
Issue number1
Early online date20 Dec 2018
Publication statusPublished - Jan 2020


  • Agile
  • genetic algorithm
  • optimization
  • team size

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Artificial Intelligence


Dive into the research topics of 'Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices'. Together they form a unique fingerprint.

Cite this