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Calvez, B. and Hutzler, G. (2005) Automatic Tuning of Agent-Based Models Using Genetic Algorithms. In: Sichman, J.S. and Antunes, L., Eds., Multi-Agent-Based Simulation VI. MABS 2005. Lecture Notes in Computer Science, Vol. 3891, Springer, Berlin, Heidelberg, 41-57.

has been cited by the following article:

• TITLE: Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

  AUTHORS: Wissam Alobaidi, Eric Sandgren, Entidhar Alkuam

  KEYWORDS: Decision Support, Multiple Goal, Agent Based, Genetic Optimization, Bin Packing

  JOURNAL NAME: Intelligent Information Management, Vol.9 No.3, May 26, 2017

  ABSTRACT: Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.