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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 ...

Fitness Values Calculation in Metaheuristics | Krill Herd Optimizer |

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Video Chapters: Krill Herd Optimization Algorithm Introduction: 00:00 KHO Parameters: 00:51 Krill's Position Initialization: 01:51 Objective Function Calculation: 03:52 Conclusion: 05:22 Learn How to Calculate Objective Function values for Metaheuristic Optimization Algorithm. Objective Function is also known as Cost Function, Fitness function, or Evaluation Function. Krill herd Optimization Algorithm Introduction, Numerical Examples: https://www.youtube.com/playlist?list=PLVLAu9B7VtkYR8GkHtTHV83AlR0WjGCfi Initialize the position for search agents randomly in the search space using this equation: Agent's Position in the Search Space : Using any Objective Function to calculate fitness values for each agent: Sphere Function is used here Fitness Values for each agent: Fitness(1) = 4.11424
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