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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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Metaheuristic Optimization Algorithms

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 Optimization Engineering - Metaheuristic Optimization Algorithms

Optimization plays a very important role in science and engineering. Optimization aim is to find out the minimum or maximum value using any objective function or cost function. In optimization different Metaheuristic Algorithms are used to solve complex problems in various fields such as Engineering Problems, Medical Problems, Computer Problems, and different real-life problems that can not be solved using classical methods. Metaheuristic optimization algorithms are classified into two main categories as Single-based optimization algorithms and Population-based optimization algorithms. 

Single-based Meta-heuristic algorithms are also known as Trajectory Algorithms. Single-based metaheuristic algorithms provide the single solution in every iteration. Single-based Metaheuristic algorithm examples: Tabu Search, Guided Local Search, Iterated Local Search, Stochastic Local Search, Iterated Local Search, Variable neighborhood search, Greedy Randomized Local Search. 

Population-based algorithms provide multiple solutions in every generation/iteration. Population-based algorithms are categorized as Nature-Inspired Algorithms, Evolutionary Algorithms, and Art Inspired Algorithms.
Nature Inspired Algorithms are further sub-categorized as:
  1. Bio-Inspired Algorithms
  2. Human-Based Algorithms
  3. Physics-Based Algorithms
  4. Chemistry Based Algorithms
  5. Plant-Based Algorithms
  6. Swarm-Based Algorithms
Let try to understand one-by-one all these population-based metaheuristic algorithms. 

EVOLUTIONARY ALGORITHMS: As we know evolution means growth or development. The result of evolution is life on earth. Four billion years ago life has begun on earth. Different stages of life on earth such as Organisms, Multi-cellular Organisms, Fish, Dragonfly, Birds, Mammals, Bees, Flowers, Humans. The evolution process occurs by Natural Selection. The evolution process includes Natural Selection, Reproduction, and Mutation. Evolutionary algorithms are a sub-category of population-based metaheuristic optimization algorithms. Evolutionary algorithms include Genetic Algorithms, Genetic Programming, Evolution Strategy, Evolutionary Programming, Differential Evolution, and Biography based optimizers.  

PHYSICS BASED ALGORITHMS: Physics-based metaheuristic algorithms are inspired by physics rules. Physics-Based Algorithms include Multiverse Optimization Algorithm (MOA), Gravitational Search Algorithm (GSA), Black Hole Optimization Algorithm (BHO), Thermal Exchange Optimization, Big-Bang Big Crunch Algorithm, Water Evaporation Optimization Algorithms, Central Force Optimization Algorithm, Arithmetic Optimization Algorithm, and Optics Inspired Algorithms. 

SWARM-BASED ALGORITHMS: Swarm-based optimization algorithms are inspired by nature. Swarm-based optimization algorithms are basically inspired by birds, ants, bees, etc. Swarm based optimization algorithm includes Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO) Algorithm, Artificial Bee Colony Optimization Algorithm (ABC), Bee Colony Optimization, Cuckoo Search Algorithm, Grey Wolf Optimization (GWO) Algorithm, Firefly Optimization Algorithm, Bat Algorithm, Whale Optimization Algorithm (WOA), Crow Search Algorithm (CSA), Dragonfly Optimization Algorithm, Grasshopper Optimization Algorithm (GOA), Harris Hawk Optimization Algorithm, Coyote Optimization Algorithm and other.

LEARN METAHEURISTIC OPTIMIZATION ALGORITHM STEP-BY-STEP WITH NUMERICAL EXAMPLES: VIDEO LINK CLICK HERE TO WATCH NOW

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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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

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Bat algorithm Explanation Step by Step with example

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Grey Wolf Optimization Algorithm Numerical Example

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