Learning the optimum as a Nash equilibrium

✍ Scribed by Süheyla Özyıldırım; Nedim M. Alemdar

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 232 KB
• Volume: 24
• Category: Article
• ISSN: 0165-1889
• DOI: 10.1016/s0165-1889(99)00012-3

✦ Synopsis

This paper shows the computational bene"ts of a game theoretic approach to optimization of high dimensional control problems. A dynamic noncooperative game framework is adopted to partition the control space and to search the optimum as the equilibrium of a k-person dynamic game played by k-parallel genetic algorithms. When there are multiple inputs, we delegate control authority over a set of control variables exclusively to one player so that k arti"cially intelligent players explore and communicate to learn the global optimum as the Nash equilibrium. In the case of a single input, each player's decision authority becomes active on exclusive sets of dates so that k GAs construct the optimal control trajectory as the equilibrium of evolving best-to-date responses. Sample problems are provided to demonstrate the gains in computational speed and accuracy.