Nwafor Livinus Anayo
@livinus_anayo
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I passionately discuss #Tech and #Algorithms.
Abakaliki, Nigeria
Joined February 2019
RT @PA_Climate: In the fight against climate change, we must prioritize equity. Let the Global South breathe. 🌍✨ As we address climate chal….
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RT @Fredluggard: Looking to learn a tech skill or break into tech? .Then Learnable is for you. Looking back now to where I was last year be….
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RT @Xandraly_: Hey Dear.Have yourself a good watch. 👇.How love started through books, watch and learn the his….
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Why use Firefly Algorithm?. 1. It is efficient in solving complex Optimization problems. 2. FA is adaptable and can handle a wide range of problem types. 3. It can be combined with other techniques to enhance performance.
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Randomness. In solving any Optimization problem using the FA algorithm, randomness must be introduced. This concept allows for exploration of a search space and prevents the algorithm from getting stuck in local optima.
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The Concept of Distance. Optimization of the solutions in the search space is influenced by distance. Solutions move towards nearby solutions that are brighter, thus simulating the attraction between fireflies that are closer to each other.
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Attractiveness of Fireflies. In Firefly algorithm, solutions in a search space are likened to fireflies. The fitness or quality of any solution is represented by it’s brightness. Thus, for the FA algorithm, fireflies move towards brighter neighbors to find better solutions.
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Key Concepts of the Firefly Algorithm. 1. Attractiveness of fireflies.2. The concept of Distance. 3. Randomness.
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Dr. Yang saw the potential to apply the phenomena exhibited by fireflies in their natural habitat to optimization problems. He envisioned a large population of solutions being optimized by the attraction of individual solutions to more optimal ones just like with fireflies.
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In nature, fireflies give out flashes of light. These flashes are used in communicating, attracting mates, and establishing social hierarchies in this species. These flashing patterns serve as signals, and the brightness of the flash determines the attractiveness of the firefly.
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FA is used to solve complex optimization problems in various fields, such as engineering, computer science, and finance.
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The Firefly Algorithm (FA) is a nature-inspired algorithm that was developed by Dr. Xin-She Yang in 2008. Just like every nature-inspired algorithm, this algorithm mimics a natural civilization – in this case, the flashing behavior of Fireflies.
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🎉✨ Ever seen fireflies light up the night sky with their magical glow? 🌟✨.Well, giving out an amazing glow isn't all that fireflies do. Their flickering in a group can help us solve problems!. 🧚♂️🔍 Let's unravel this magic! 📈💡
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Still on applying firefly algorithm (FA) to optimization problems. This video explains how to search for the global best solution. #NLA #ai #ml #fireflyalgorithm #algorithms #globaloptimum #insectbehaviour #science #explorationvsexploitation #problemsolving #optimization
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The future of the FA. The application of the Firefly Algorithm in machine learning is a dynamic field with ongoing research and development. Its future holds promise as it continues to be researched and refined to meet the evolving demands of the ML community.
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3. Benchmarks. Researchers have applied the Firefly Algorithm to benchmark and optimize machine learning models. It is used to improve the accuracy and convergence of models, making them more effective in real-world applications.
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2. Hyperparameter tuning. The Firefly Algorithm can be used to automatically search for the best combination of hyperparameters, which is often a time-consuming and challenging task.
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Ways of applying FA to machine learning. 1. Feature Selection and Dimensionality Reduction.FA can help identify the most relevant features in a dataset, thereby reducing the dimensionality and improving the efficiency and performance of ML models.
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