This course has been designed to introduce you to a field of programming you might have never been exposed to. In this course, you’ll learn everything you need to know to start working with genetic algorithms. As you work through the course, you’ll build a framework for problems using genetic algorithms. By the end, you’ll have a full-featured, customizable framework complete with statistics, genealogy tracking, and more, and you’ll have learned everything you need to solve practical problems with genetic algorithms.
This course has been designed to introduce you to a field of programming you might have never been exposed to. In this course, y...Show More
WHAT YOU'LL LEARN
Learn about the basics of Genetic Algorithms in Elixir.
Learn how to design the framework for using Genetic Algorithms.
Explore the processes of selection, crossover, mutation and reinsertion.
Analyze the performance of the Genetic Algorithms by benching and profiling them.
Explore the different ways of visualizing Genetic Algorithms along with testing and type checking your code.
Learn about the basics of Genetic Algorithms in Elixir.
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Content
1.
Introduction
3 Lessons
Get familiar with Genetic Algorithms in Elixir, their applications, and Elixir's advantages.
2.
Writing Your First Genetic Algorithm
6 Lessons
Look at using Elixir to build genetic algorithms for optimization and problem-solving.
3.
Breaking Down Genetic Algorithms
6 Lessons
Work your way through the fundamental structure and processes of genetic algorithms in Elixir.
4.
Encoding Problems and Solutions
6 Lessons
Grasp the fundamentals of enhancing genetic algorithms with Elixir's structs and behaviours.
5.
Evaluating Solutions and Populations
6 Lessons
Take a closer look at optimizing cargo loads, penalty functions, termination criteria, and crafting fitness functions.
6.
Selecting the Best
5 Lessons
Incorporate various selection strategies in genetic algorithms to balance diversity and fitness.
7.
Generating New Solutions
7 Lessons
Piece together the parts of effective crossover strategies to enhance genetic algorithm solutions.
8.
Preventing Premature Convergence
5 Lessons
Break down methods to prevent premature convergence using mutation strategies and diverse techniques.
9.
Replacing and Transitioning
5 Lessons
Unpack the core of genetic algorithms for class scheduling, reinsertion, and population management strategies.
10.
Tracking Genetic Algorithms
6 Lessons
Examine genetic algorithm simulations and track evolution statistics, genealogy trees, and adaptive traits.
11.
Visualizing the Results
4 Lessons
Apply your skills to visualizing genetic algorithm results and creating AI agents for Atari games.
12.
Optimizing Your Algorithms
7 Lessons
Take a closer look at optimizing genetic algorithms through benchmarking, profiling, and parallelization in Elixir.
13.
Writing Tests and Code Quality
4 Lessons
Tackle code testing, property tests, code cleanup, and type specifications for reliable Elixir frameworks.
14.
Moving Forward
4 Lessons
Build on the role of genetic algorithms in AI, real-life applications, neural networks, and advanced exploration.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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