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We present a parallel computational method for generating endogenous social networks from large-scale simulation data from the Chicago Social Interaction Model (chiSIM). The model scope aims to simulate the population of the entire city of Chicago which includes approximately 2.9 million discrete individuals. Generated person collocation networks contain more than 106 person nodes and more than 109...
Coalescent genealogy samplers are effective tools for the study of population genetics. They are used to estimate the historical parameters of a population based upon the sampling of present-day genetic information. A popular approach employs Markov chain Monte Carlo (MCMC) methods. While effective, these methods are very computationally intensive, often taking weeks to run. Although attempts have...
Infection, replication and mutation govern the population dynamics of viruses and are the key mechanisms driving their evolution. In particular, RNA viruses (such as the causative agents of Ebola, Dengue, Zika, West Nile, and SARS) have the highest mutation rates which enable them to form highly diverse populations within a single host, evade immune responses and develop resistances to drugs. Understanding...
Handling constraints is not a trivial task in evolutionary computing. Even if different techniques have been proposed in the literature, very few have considered co-evolution which tends to decompose problems into easier sub-problems. Existing co-evolutionary approaches have been mainly used to separate the decision vector. In this article we propose a different co-evolutionary approach, referred...
In this work, a framework based on maximum likelihood estimation and mutual information is proposed to design a metaheuristic. A multilevel decomposition of metaheuristics is proposed that allow to have a unified vision on this optimization approach. Then, a new layer based on machine learning is added to take profit from the evolution of the algorithm to adapt it to the considered problem to alleviate...
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