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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...
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Hybrid Grasshopper Optimization Algorithm with Genetic Algorithm

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Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm


GOA-GA Hybrid Metaheuristics Video Chapters: Introduction: 00:00 Hybrid Metaheuristics: 01:11 Metaheuristic Hybridization Types: 02:47 Hybrid Grasshopper Optimization Algorithm - Genetic Algorithm: 04:06 Hybrid Grasshopper and Genetic Algorithm Steps: 08:54 Conclusion: 13:00

Hybrid methods are powerful as compared to others. Suppose, we have 2 Algorithms: Algorithm A and Algorithm B. Now suppose we merge the merits of both algorithms and formed Hybrid A-B Algorithm. New Algorithm i.e., Hybrid A-B Algorithm is better as compared to Algorithm A or Algorithm B.

Metaheuristics Hybridization Types:
  • Metaheuristic with Metaheuristic.
  • Metaheuristic with Exact Methods.
  • Metaheuristic with Constraint Programming, Artificial Intelligence.
  • Metaheuristic with Data Mining and Machine Learning Techniques.
Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm |Hybrid G.O.A - G.A.|
  1. Hybrid GOA-GA is a combination of 2 Meta-heuristics.
  2. Author Combined the merits of the Grasshopper Optimization Algorithm and Genetic Algorithm and created Hybrid Grasshopper Optimization Algorithm with Genetic Algorithm to solve the system of non-linear equations.
  3. Metaheuristic Algorithm Challenges: Large Search Space, Variables, Parameters, Algorithm trapped into local optima, High Computation Cost. Hybrid Meta-heuristics are used to overcome such problems.
  4. In Hybrid GOA-GA , Genetic algorithm is used to prevent the algorithm to trapped in local optima. And Grasshopper algorithm is used for the Global search.
  5. Grasshopper algorithm is used for Exploration Phase (i.e., Global Search) and the Genetic Algorithm is used for the Exploitation phase (i.e., Local Search).



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