Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

Inclusive Fitness

Inclusive Fitness

Most altruistic behavior—in which one animal helps another at a cost to themselves—occurs between relatives. Scientists think these altruistic behaviors evolved because they increase the inclusive fitness of the animal providing help.

Hybrid Zones

Hybrid Zones

Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application.

Bioengineering (Basel, Switzerland)·2026

Same author

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System.

Sensors (Basel, Switzerland)·2022

Same author

Radio Frequency over Fibre Optics Repeater for Mission-Critical Communications: Design, Execution and Test.

Sensors (Basel, Switzerland)·2022

Same author

Analysis of Compromising Video Disturbances through Power Line.

Sensors (Basel, Switzerland)·2022

Same author

Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles.

Sensors (Basel, Switzerland)·2021

Search research articles

Related Experiment Video

Updated: Sep 5, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and

Seyed Salar Sefati¹, Simona Halunga¹

¹Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 București, Romania.

Sensors (Basel, Switzerland)

|July 9, 2022

Summary

This study introduces a novel Artificial Bee Colony and Genetic Algorithm (ABCGA) for optimizing cloud computing service composition. The method efficiently selects services to meet diverse user needs, enhancing reliability, availability, and cost-effectiveness.

Keywords:

adaptive penalty function genetic algorithm artificial bee colony cloud computing quality of service (QoS)service composition

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Related Experiment Videos

Last Updated: Sep 5, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Computer Science
Cloud Computing
Artificial Intelligence

Background:

Cloud Computing (CC) offers numerous services, but composing them to meet complex user requirements presents a significant Quality of Service (QoS) challenge.
Single cloud services often cannot fulfill diverse real-world needs, necessitating the integration of multiple services.
Selecting and composing services is an NP-hard optimization problem due to the vast number of available services and their QoS attributes.

Purpose of the Study:

To address the NP-hard problem of cloud service composition by developing an efficient metaheuristic algorithm.
To enhance the integration of existing cloud services to meet intricate user necessities.
To improve the reliability, availability, and cost-effectiveness of cloud service composition.

Main Methods:

A hybrid metaheuristic algorithm, Artificial Bee Colony and Genetic Algorithm (ABCGA), is proposed for cloud service composition.
The Genetic Algorithm (GA) is used to select services based on fitness functions.
The Artificial Bee Colony (ABC) algorithm further refines the selection process to match specific user needs.

Main Results:

The proposed ABCGA method demonstrates efficiency in cloud service composition.
Experimental evaluation using Cloud SIM simulation validates the algorithm's performance.
The results show significant improvements in reliability, availability, and cost compared to existing methods.

Conclusions:

The ABCGA algorithm offers an effective solution for the complex challenge of cloud service composition.
The hybrid approach successfully balances QoS parameters to meet diverse user requirements.
This research contributes to more efficient and reliable cloud service integration.