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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 ...

Black Hole Optimization Algorithm Step-by-Step with Example ~xRay Pixy

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 Black Hole Optimization Algorithm || BHO Algorithm The black hole algorithm is a Natural Heuristic Algorithm by simulating the ‘‘Black Hole’’ phenomenon in the universe.  Black Hole Optimization Algorithm is a new bio-inspired metaheuristic approach based on the observable fact of black hole phenomena. Black Hole : Region in the space where Gravity is so Strong. No object can escape from its powerful gravitational pull. Black holes are formed in the  space when star of massive size collapses. Anything falls into black hole is forever gone from our universe. Black Hole Optimization Algorithm Steps: Step 01: Randomly Initialize population for N candidate solutions in the search space. Consider Stars as initial population and each start is candidate solution. And Best  Candidate among all (i.e., Best Solution) is considered as Black Hole. Step 02: Calculate Fitness Values for each agent in the current population. Calculate fitness values for each candidate and best am...
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