100% Free-Optimization Using Genetic Algorithms : MATLAB Programming

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A Quick Way to Learn and Solve Optimization Problems in MATLAB. A Course for Beginners.

What you’ll learn

  • Implementation of Genetic Algorithm in MATLAB
  • Analyze the performance of the Genetic Algorithm
  • Modify or improve the Genetic Algorithm
  • Specifying objective functions
  • Specifying constraints
  • Vectorizing objective function and constraints
  • Obtaining local and global optima
  • Parallel computing

Requirements

  • MATLAB installed in your laptop/desktop computer

Description

There has been a rapidly growing interest in a field called Genetic Algorithms during the last thirty years. Have you ever wondered how specific theories greatly inspire a particular invention?. The same goes with Genetic Algorithms. All of us would have heard of the famous view of Charles Darwin, “Survival of the fittest”, which extends to Evolution by Natural Selection. Inspired by Darwin’s theory, the Genetic Algorithm is a part of Evolutionary Algorithms, specifically to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover, and selection. The Genetic Algorithm can be easily applied to different applications, including Machine Learning, Data Science, Neural Networks, and Deep Learning.

This course will teach you to implement genetic algorithm-based optimization in the MATLAB environment, focusing on using the Global Optimization Toolbox. Various kinds of optimization problems are solved in this course.  At the end of this course, you will implement and utilize genetic algorithms to solve your optimization problems. The complete MATLAB programs included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Take advantage of learning and understanding the fast-growing field of evolutionary computation.

Happy learning.

Who this course is for:

  • Anyone who is interested to solve optimization problems.
  • Researchers who want to publish ISI papers in this field.
  • Students who are working on optimization problems.

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