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Nash Equilibrium In Game Theory ~xRay Pixy

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 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisoner’s Dilemma  07:17  Prisoner’s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, it’s a Nash Equilibrium Prisoner’s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...
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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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