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A. Douin

Publications and source records attributed to A. Douin.

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Memory effects govern scale-free dynamics beyond universality classes

Scale-invariant avalanches -- with events of all sizes following power-law distributions -- are considered critical. Above the upper critical dimension of four, the mean-field solution with a robust $3/2$ size exponent describes the dynamics. In two and three dimensions, spatial constraints yield smaller yet robust exponent values governed by universality classes. However, both earthquake data and experiments often show exponent values larger than $3/2$, challenging those theoretical arguments based on critical behavior. Through extensive simulations in the classical OFC earthquake model, here we show a clear transition from the theoretical expected behavior of a robust exponent value, to a regime of quasi-critical dynamics with larger than $3/2$ exponents that depend on dissipation. While the first critical regime exhibits an inherently memoryless behavior, both the transition and the second regime are driven by memory effects provoked by the growth of avalanches over the traces left by previous events, due to dissipative mechanisms. The system hovers at a distance $d_{cp}$ from the critical point, and accounting for a power-law distribution of $d_{cp}$, validated by susceptibility measurements, captures the transition. This framework provides a unified description of both critical and quasi-critical behavior, and thus of the full spectrum of scale-free dynamics observed in nature.

cond-mat.stat-mech

Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms

By combining Genetic Programming, MAP-Elites and Covariance Matrix Adaptation Evolution Strategy, we demonstrate very high success rates in Symbolic Regression problems. MAP-Elites is used to improve exploration while preserving diversity and avoiding premature convergence and bloat. Then, a Covariance Matrix Adaptation-Evolution Strategy is used to evaluate free scalars through a non-gradient-based black-box optimizer. Although this evaluation approach is not computationally scalable to high dimensional problems, our algorithm is able to find exactly most of the $31$ targets extracted from the literature on which we evaluate it.

cs.NE