Towards incremental social learning in optimization and multiagent systems

Social learning is a mechanism that allows individuals to acquire knowledge from others without incurring the costs of acquiring it individually. Individuals that learn socially can thus spend their time and energy exploiting their knowledge or learning new things. In this paper, we adapt these ideas for their application to both optimization and multiagent learning. The approach consists of a growing population of agents that learn socially as they become part of the main population. We find that learning socially in an incremental way can speed up the optimization and learning processes, as well as improve the quality of the solutions and strategies found.

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Marco Antonio Montes de Oca, Thomas Stützle
• Keywords: GECCO 2008, Main Population, Multiagent Learning, Optimization, Social Learning