Distributed problem solving by memetic networks
Ricardo M. Araújo, Luís C. Lamb · 2008
This paper illustrates the use of a novel class of population-based optimization algorithms namely \textsl{Memetic Networks}. These algorithms make use of an underlying network to structure information flow between multiple individuals representing points in the search space. Memetic Networks have as a fundamental characteristic the possibility to aggregate several solutions in order to compose new ones. Network properties allow to control how information is spread among the population. We apply these algorithms to several real-valued benchmark optimization problems and the TSP and report results from extensive simulations. We show how some network properties can influence the algorithm's performance and illustrate the effectiveness of this new class of algorithms.