Learning Genetic Algorithms with Python
There is a lot of talk about Machine Learning, Deep Learning,Neural Networks, and many other terms and technologies that provides Artificial Intelligence in our life. These technologies are used everywhere in our daily life. Image and terrain recognition techniques are being used in autonomous vehicles that are already driving worldwide. And the number of autonomous cars is
increasing at a tremendous rate.
Artificial intelligence frameworks and libraries are beginning to penetrate very deeply into all areas of programming. An ordinary programmer needs to have at least a basic understanding of what machine learning is, what kind of tasks it solves, and how to work with it. Machine learning contains a broad set of tools. Most
of the books and teaching materials describe only a few of them, while the other approaches based on the evolutionary search for solving problems remain uncovered.
This book is about Genetic Algorithms. It tells how the principles formulated by Charles Darwin in his book “The Origin of Species” in 1859 in our time help to solve the most complex problems that contain billions and billions of solutions. Genetic Algorithms solve problems that cannot be solved analytically. It mimics the process
of evolution and natural selection in a population, where each individual represents a solution to a problem. The more vital individuals from the population outlive the weaker ones and produce the next generation of individuals. So after the evolution process, after many generations, we get some solution to the problem, which will highly likely satisfy the requirements.
The main disadvantage of most machine learning tools can beformulated following way:
“They solve problems, but they don’t solve the problem - how to solve problems.”
Genetic algorithms provide a method for solving a problem, i.e.,how to solve problems in the absence of human experience. Genetic algorithms are a relatively simple and very effective method for solving a large class of problems. They are intuitive, simple, and can be an excellent introduction to machine learning.
The rapid development in AI is made possible for humans to obtain solution to abstract problems. Complex computational problems that are very difficult to solve by classical methods can now be solved by AI.
One of the most powerful techniques to solve such complex problems is genetic algorithms (GA), which is based on the principle of an evolutionary approach.
In the late 60s, American researcher J. Holland proposed to find solutions to optimization problems using methods and evolution models of animal populations in nature. Since the evolution’s basic laws were investigated and described by genetics, the proposed approach was called genetic algorithms. GA is a randomly directed search algorithm based on mechanisms of natural selection and natural genetics. It implements the principle of survival of the fittest, forming and changing the search algorithm based on evolutionary modeling.
DOWNLOAD :- HERE