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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 ...

Learn How to Apply Ant Colony Optimization to Traveling Salesman Problem...

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Ant Colony Optimization Metaheuristics for the Traveling Salesman Problem Numerical Example In this video you will learn, How to Solve Traveling Salesman Problem (TSP) using Ant Colony Optimization Algorithm (ACO). Ant System for the Traveling Salesman Problem Chapters: Introduction : 00:00 Traveling Salesman Problem (TSP): 00:33 Ant Colony Optimization Traveling Salesman Problem Example: 02:11 Distance Calculation between Cities: 04:30 Solution Construction for Artificial Ants: 06:45 Ants Tour Comparison for TSP: 14:52 Conclusion: 15:45 Part 1: Learn Ant Colony Optimization Algorithm Ant Colony Optimization Algorithm step-by-step with Example (ACO) ~xRay Pixy https://youtu.be/qYXeMFCf1Gk Ant System for the Traveling Salesman Problem. Ant Colony Optimization is a Nature Inspired Approach. Ant Colony Optimization (ACO) algorithm is basically inspired by the foraging behavior of ants searching for suitable paths between their colonies and food source. Ant Colony O...
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