40,548 research outputs found
Resonant Activation of Population Extinctions
Understanding the mechanisms governing population extinctions is of key
importance to many problems in ecology and evolution. Stochastic factors are
known to play a central role in extinction, but the interactions between a
population's demographic stochasticity and environmental noise remain poorly
understood. Here, we model environmental forcing as a stochastic fluctuation
between two states, one with a higher death rate than the other. We find that
in general there exists a rate of fluctuations that minimizes the mean time to
extinction, a phenomenon previously dubbed "resonant activation." We develop a
heuristic description of the phenomenon, together with a criterion for the
existence of resonant activation. Specifically the minimum extinction time
arises as a result of the system approaching a scenario wherein the severity of
rare events is balanced by the time interval between them. We discuss our
findings within the context of more general forms of environmental noise, and
suggest potential applications to evolutionary models.Comment: 12 pages, 7 Figures, Accepted for publication in Physical Review
Analysis of Different Types of Regret in Continuous Noisy Optimization
The performance measure of an algorithm is a crucial part of its analysis.
The performance can be determined by the study on the convergence rate of the
algorithm in question. It is necessary to study some (hopefully convergent)
sequence that will measure how "good" is the approximated optimum compared to
the real optimum. The concept of Regret is widely used in the bandit literature
for assessing the performance of an algorithm. The same concept is also used in
the framework of optimization algorithms, sometimes under other names or
without a specific name. And the numerical evaluation of convergence rate of
noisy algorithms often involves approximations of regrets. We discuss here two
types of approximations of Simple Regret used in practice for the evaluation of
algorithms for noisy optimization. We use specific algorithms of different
nature and the noisy sphere function to show the following results. The
approximation of Simple Regret, termed here Approximate Simple Regret, used in
some optimization testbeds, fails to estimate the Simple Regret convergence
rate. We also discuss a recent new approximation of Simple Regret, that we term
Robust Simple Regret, and show its advantages and disadvantages.Comment: Genetic and Evolutionary Computation Conference 2016, Jul 2016,
Denver, United States. 201
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