A Gentle Introduction to Genetic Algorithms with Python and DEAP скачать в хорошем качестве

A Gentle Introduction to Genetic Algorithms with Python and DEAP

Content summary: Genetic algorithms, inspired by natural selection, are powerful tools used to solve optimization problems in ways that mimic evolution. This session will unravel the fundamental principles and essential components of genetic algorithms, including selection, crossover, and mutation methods. We will explore how these methods are implemented using the DEAP module, a popular Python library specifically designed for evolutionary algorithms. Learning Objectives: 1. Understand the Basics: Grasp the core principles of genetic algorithms, including their structure and the theory behind their evolutionary processes. 2. Explore Key Techniques: Learn about various selection methods like roulette wheel and tournament selection, crossover techniques, and mutation processes that drive the diversity and solution quality in genetic algorithms. 3. Apply Real-World Examples: Using the DAEP module, we will tackle two classic optimization problems: the knapsack problem and the traveling salesman problem. This hands-on approach will help solidify your understanding by seeing genetic algorithms in action. 4. Build Practical Skills: Gain the ability to implement genetic algorithms in Python, enhancing your toolkit for solving complex optimization challenges in your field. Join us for this informative session and unlock the potential of genetic algorithms to optimize solutions in an array of applications. Presenter: Ruopeng An Code used in this video can be downloaded from GitHub: 240503 Genetic Algorithms With DEAP.pdf; 240503_knapsack.zip; 240503_tsp.zip https://github.com/DreamJarsAI/Apply-... Hashtags: #artificialintelligence #machinelearning #deeplearning #python #pythonprogramming #pythontutorial #aitutorial #coding #neuralnetworks #neuralnetwork #pytorch #computervision #nlp #naturallanguageprocessing #scikitlearn