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John Koza, a Stanford University researcher, developed genetic programming as a method to evolve computer programs by simulating the natural selection process. In this approach, a population of computer programs, composed of primitive functions and terminals, is evolved to solve a given problem. Each program's fitness is determined by its effectiveness in solving the problem. A few programs with high fitness are selected for reproduction, while many participate in a recombination operation called crossover. By iterating this process over multiple generations, the structure of a computer program that effectively solves the problem can emerge.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #Part_1

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429 viewsNaser Pakar, edited  May 4, 2023 at 06:40

John Koza, a Stanford University researcher, developed genetic programming as a method to evolve computer programs by simulating the natural selection process. In this approach, a population of computer programs, composed of primitive functions and terminals, is evolved to solve a given problem. Each program's fitness is determined by its effectiveness in solving the problem. A few programs with high fitness are selected for reproduction, while many participate in a recombination operation called crossover. By iterating this process over multiple generations, the structure of a computer program that effectively solves the problem can emerge.
Download from Iranian server (VPN and proxy must be disconnected)
This video has Persian subtitles and noise removal and quality enhancement.
🆔 @MATLAB_House

@MATLABHOUSE

#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #Part_1

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