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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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All Members-Based Optimizer (AMBO) || STEP-BY-STEP || ~xRay Pixy

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All Members-Based Optimizer (AMBO) Learn All Members-Based Optimizer Step-by-Step with Examples. Algorithm Type: Metaheuristic Optimization Technique Algorithm Main Idea: Make more use of the Population Matrix. Tested on Different Benchmark Test Functions. Algorithm Performance: Provide Better results in comparison with different metaheuristic optimization algorithms. Used for Solving Optimization Problems. ALGORITHM MAIN IDEA Make use of the Population Matrix and All Members can play role in Updating Algorithm Population. ALL MEMBERS-BASED OPTIMIZER STEPS STEP 01: Initialize Algorithm Important Parameters. STEP 02: Initialize Population Randomly in the Search Space. STEP 03: Evaluate Initial Population using the Fitness Function. STEP 04: Check While (Current Iteration < Maximum Iteration) Do STEP 05: Update Members Position and Best Member Position. STEP 06: Update Population Members using STAGE 01. STEP 07: Update Population Members using STAGE 02. STEP 08: Save Best Solut...
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