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Related Concept Videos

Pollination and Flower Structure02:40

Pollination and Flower Structure

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Flowers are the reproductive, seed-producing structures of angiosperms. Typically, flowers consist of sepals, petals, stamens, and carpels. Sepals and petals are the vegetative flower organs. Stamens and carpels are the reproductive organs.  
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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.
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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).
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Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
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Related Experiment Video

Updated: Aug 13, 2025

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea
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A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution.

Yunjian Jia1, Shankun Wang1, Liang Liang1

  • 1School of Electronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

The improved flower pollination algorithm (FPA-CCDE) enhances optimization by integrating cross-generation differential evolution and adaptive parameters. This novel approach boosts convergence speed and reduces local optima for better performance in complex problems like robot path planning.

Keywords:
cross-generationdifferential evolution (DE)external archiveflower pollination algorithm (FPA)robot path planningroulette wheel

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms

Background:

  • The Flower Pollination Algorithm (FPA) is a nature-inspired heuristic optimization method.
  • Standard FPA exhibits sensitivity in global and local search processes to parameters and direction.

Purpose of the Study:

  • To address the limitations of the standard FPA.
  • To propose an improved Flower Pollination Algorithm with Cross-Generation Differential Evolution (FPA-CCDE).

Main Methods:

  • Implemented cross-generation differential evolution for guided local search.
  • Introduced cosine inertia weights for accelerated convergence.
  • Utilized external archiving and adaptive parameter adjustments (scaling factor, crossover probability).
  • Incorporated cross-generation roulette wheel selection to mitigate local optima entrapment.

Main Results:

  • FPA-CCDE demonstrated superior stability and optimization capabilities compared to five state-of-the-art algorithms on benchmark functions.
  • Applied to robot path planning, FPA-CCDE achieved low cost, high efficiency, and robustness.
  • The algorithm showed effectiveness in enhancing population richness and reducing local solutions.

Conclusions:

  • FPA-CCDE offers significant improvements over the standard FPA.
  • The proposed algorithm is effective for complex optimization tasks, including robot path planning.
  • FPA-CCDE shows promise for various intelligent system applications.