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.
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