Learning Outcomes

In this free course, you will learn how to:

• Describe what Crossover and Mutation in Genetic Algorithms is and which methods are used to implement them
• Calculate and implement Real-Coded Genetic Algorithm in Python from scratch and step by step
• Outline Crossover and Mutation along with their different methods in the main loop of GA in MATLAB and Python
• Evaluate and Fine tuning the final GA codes in MATLAB and Python to get better results
• Define Evolutionary Algorithms and Genetic Algorithms (GAs)
• Discuss the selection phase in GAs and the different methods along with the Merging, Sorting, and Selection phase of GA
• Compute and implement both Binary and Real-Coded Genetic Algorithms in MATLAB from scratch and step by step
• Arrange the initialization phase of Binary and Real-Coded Genetic Algorithm in MATLAB and only Real-Coded GA in Python
• Solve the selection phase in the main loop of GA in MATLAB and Python using Random or Roulette Wheel Selection methods
• Apply and perform the Merging, Sorting, and Selection phase of GA both in Python and MATLAB
• Evaluate and run Binary and Real Coded GA in MATLAB and run and get results from Real Coded GA in Python
• Recognize and implement the Main Loop of Iteration in Binary and Real-Coded GA in MATLAB and Real-Coded GA in Python