I like what I know: How recognition-based decisions can structure the environment

Abstract. Cognitive mechanisms are shaped by evolution to match their environments. But through their use, these mechanisms exert a shaping force on their surroundings as well. Here we explore this cognition-environment interaction by looking at how using a very simple cognitive mechanism, the recognition heuristic for making choices, can result in some objects in the world being much more often recognized and “talked about ” than others. An agent-based simulation is used to show what behavioral factors affect the emergence of this environmental structure.

Incremental particle swarm-guided local search for continuous optimization

We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provides rapid convergence to good solutions while the particle swarm algorithm enables a comprehensive exploration of the search space. We provide experimental evidence that shows that the algorithm can find good solutions very rapidly without compromising its global search capabilities. © 2008 Springer Berlin Heidelberg.SCOPUS: cp.k5th International Workshop on Hybrid Metaheuristics, HM 2008; Malaga; Spain; 8 October 2008 through 9 October 2008.info:eu-repo/semantics/publishe