Most of evolutionary algorithms (EAs) are based on a fixed population. However, due to this feature, such algorithms do not fully explore the potential of searching ability and are time consuming. This paper presents a novel nature-inspired heuristic optimization algorithm: bacterial foraging algorithm with varying population (BFAVP), based on a more bacterially-realistic model of bacterial foraging patterns, which incorporates a varying population framework and the underlying mechanisms of bacterial chemotaxis, metabolism, proliferation, elimination and quorum sensing. In order to evaluate its merits, BFAVP has been tested on several benchmark functions and the results show that it performs better than other popularly used EAs, in terms of both accuracy and convergency.
We study numerically how the
Separation process depends on the specific motility strategies of the
Microorganisms involved. Crucial properties such as the separation efficiency
And the separation time for two bacterial strains are precisely defined and
Evaluated. In particular, the sorting of two ba...
We review recent progress into neural circuit analysis of hunger in the mouse by focusing on a starvation-sensitive neuron population in the hypothalamus that is sufficient to promote voracious eating. We also consider research into the motivational processes that are thought to underlie hunger in o...
We propose a new algorithm to do posterior sampling of Kingman's coalescent,
Based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the
Algorithm is an instantiation of the Particle Gibbs Sampling method, which
Alternately samples coalescent times conditioned on coalescent tree...
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