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Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Related Experiment Video

Updated: Oct 17, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Optimization of frequency combs spectral-flatness using evolutionary algorithm.

Thyago Pinto, Uiara C de Moura, Francesco Da Ros

|October 7, 2021

Summary

This summary is machine-generated.

Meta-heuristics algorithms optimize optical frequency combs (OFCs) flatness. Particle swarm optimization and differential evolution achieved a 2 dB flatness in experimental OFC optimization.

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Quasi-light Storage for Optical Data Packets

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Published on: February 6, 2014

Area of Science:

Photonics and Laser Technology
Computational Science

Background:

Optical frequency combs (OFCs) are crucial for precise frequency measurements.
Achieving flatness in OFC spectra is essential for many applications.
Existing flatness compensation methods often require additional components.

Purpose of the Study:

To demonstrate the effectiveness of meta-heuristics algorithms for OFC flatness optimization.
To explore flatness optimization of a laser source without external compensation components.
To investigate the use of optimized bias current and multi-harmonic RF driving signals.

Main Methods:

Utilized particle swarm optimization (PSO) and differential evolution (DE) algorithms.
Optimized laser bias current amplitude, RF harmonic amplitudes, and relative phases.
Performed numerical simulations and online experimental optimization.

Main Results:

Achieved a 9-line Gaussian-filtered (GS) laser-based OFC spectrum with 2.9 dB flatness via numerical optimization.
Successfully obtained a 7-line GS-laser-based OFC with 2 dB flatness through online experimental DE optimization.

Conclusions:

Meta-heuristics algorithms are effective for direct OFC flatness optimization.
Direct optimization of laser driving signals can achieve high OFC spectral flatness.
The DE algorithm shows promise for real-time OFC flatness control.