Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Genetic Programming and Evolvable Machines
Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Community detection in complex networks using collaborative evolutionary algorithms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Exploring geo-temporal differences using GTdiff
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
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Traditionally, a genetic algorithm is used to analyze networks by maximizing the modularity (Q) measure to create a favorable community. A coevolutionary algorithm is used here to not only find the appropriate community division for a network, but to find interesting networks containing substantial changes in data within a very large network space. The network is one of the largest, if not the largest, analyzed by evolutionary computation techniques to date and is created using a real world data set consisting of fisheries catch data in the north Atlantic Ocean off the coast of Canada. This work examines the quantitative performance of two types of coevolutionary algorithms against both a standard GA that uses a natural (but not necessarily optimal) division of the data set into communities, and simulated annealing. The goal for all search algorithms was to automatically find anomalies (differences in catch) within the data. To measure practical usefulness of the system, a fisheries expert analyzed the best networks located by the search algorithms using an existing visualization software prototype. The expert indicated that a refined version of coevolutionary GA known as PAMDGA was found to most reliably locate subnetworks containing catch differences of biological relevance.