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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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Crow Search Algorithm (CSA) / Crow Search Optimization (CSO) Algorithm

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What is the Crow search algorithm?

Crow search algorithm (CSA) is a population-based algorithm. Crow search algorithm is similar to Particle Swarm Optimization (PSO) algorithm. Crow search algorithm mimics the crow's intellegent behavior. CSA is based on crow's intelligent behavior. 

Key Point About Crow's

Crows live in large families and care for younger ones. They eat insects, worms, nuts, fruits, food, birds, non-insects, etc. They can hide excess food in hiding places and retrieve it when needed. Age: 14-17 years.

Crow can memorize the hiding place positions.

They follow each other to steal their food.

Crows protect their hiding places from attackers.

Two main parameters used in the CSA algorithm:

Flights Length,

Awareness Probability. 

Crow Search Optimization Algorithm Main Concepts 

Crow store excess food in hiding places and retrieve it when needed. 

Crow cheat each other (i.e., they steal each other food).


Crow Search Algorithm Step-by-Step with Example ~xRay Pixy

Crow search algorithm Video Link : Crow search algorithm

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Grey Wolf Optimization Algorithm Numerical Example

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