Yoshio Martinez, Katya Rodriguez, Carlos Gershenson
Genetic algorithms have been used in recent decades to solve a broad variety of search problems. These algorithms simulate natural selection to explore a parameter space in search of solutions for a broad variety of problems. In this paper, we explore the effects of introducing temporal heterogeneity in genetic algorithms. In particular, we set the crossover probability to be inversely proportional to the individual’s fitness, i.e., better solutions change slower than those with a lower fitness. As case studies, we apply heterogeneity to solve the N-Queens and Traveling Salesperson problems. We find that temporal heterogeneity consistently improves search without having prior knowledge of the parameter space.

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Martin Kassabov, Steven H. Strogatz, Alex Townsend
Consider n identical Kuramoto oscillators on a random graph. Specifically, consider \ER random graphs in which any two oscillators are bidirectionally coupled with unit strength, independently and at random, with probability 0≤p≤1. We say that a network is globally synchronizing if the oscillators converge to the all-in-phase synchronous state for almost all initial conditions. Is there a critical threshold for p above which global synchrony is extremely likely but below which it is extremely rare? It is suspected that a critical threshold exists and is close to the so-called connectivity threshold, namely, p∼log(n)/n for n≫1. Ling, Xu, and Bandeira made the first progress toward proving a result in this direction: they showed that if p≫log(n)/n1/3, then \ER networks of Kuramoto oscillators are globally synchronizing with high probability as n→∞. Here we improve that result by showing that p≫log2(n)/n suffices. Our estimates are explicit: for example, we can say that there is more than a 99.9996% chance that a random network with n=106 and p>0.01117 is globally synchronizing.

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İlker Türker, Serkan Aksu

Applied Acoustics
Volume 191, 30 March 2022, 108660

We propose a novel time-graph representation method for sounds and time series.
Quantized amplitude levels serve as nodes, while neighborhood of these nodes in time domain define connections.
Applying various undersampling rates, a multilayer graph representation is achieved, further composed as RGB images.
Flattened graph representations from each time frame are tiled to compose a time-graph representation.
The proposed representation improves classification accuracy when used in combination with mel-spectrograms.

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