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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 ...

Whale Optimization Algorithm for Association Rule Mining.

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 Whale Optimization Algorithm for association rule mining. Input: Number of Maximum Iteration and Population Size, Minsupport, minconfedence.  Step 01: Initialize the population size for n search agents.[Xi(i=1,2,3,...n)] Step 02: Initialize i, A, C, L, and p. Step 03: Compute the fitness value of each search agent/whale. Step 04: X* = the best rule Step 05: While (CurrentIteration <= MaximumIteration ) Step 06: Update a, A, C, L and p. Step 07: For all whale poplation check          if (p<0.5) if(|A|<1)    For each Item in the solution Xi.    Update Items. Else if(|A|=1)     Select a random Item in Xi. Update Items. End if        For each item in the solution Xi. If the Item is odd, it belongs to the antecedent, Otherwise, it belongs to the consequence. End for Step 08: Calculate the fitness of each search agent. Step 09: Update X* if there is a better solution. Ste...
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