EulerFold
CS Students & Optimization Engineers

Genetic Algorithms & Evolutionary Computation

4 weeks
0 Learners
Jul 23

Optimization without gradients: useful for scheduling and design and game AI. This comprehensive curriculum is designed to give you hands-on experience with Genetic Algorithms & Evolutionary Computation.

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About this Course

Optimization without gradients: useful for scheduling and design and game AI. This comprehensive curriculum is designed to give you hands-on experience with Genetic Algorithms & Evolutionary Computation. This CS Students & Optimization Engineers curriculum is designed to give you hands-on experience and deep conceptual understanding. Across 4 intensive modules, you'll tackle real-world challenges and build practical projects that reinforce your learning. By the end of this journey, you'll have the skills and proof of work to demonstrate your expertise.

What you'll learn

Master the core concepts of foundations of genetic algorithms & representation.
Gain hands-on experience with selection mechanisms, crossover & mutation operators.
Understand the architecture behind multi-objective optimization & nsga-ii.
Implement production-grade advanced evolutionary paradigms (gp, neat & co-evolution).

Prerequisites

intermediate Level

Requires basic familiarity with the tech stack.

  • Basic Go syntax & goroutines

Ideal for

CS Students & Optimization Engineers

CS Students & Optimization Engineers Professionals
Tech Enthusiasts
W1

Foundations of Genetic Algorithms & Representation

Master the core concepts of foundations of genetic algorithms & representation.

3 videos84m
2 readings
3 topics
1 homework
Learn

Topics

1.1
Genotypes, Phenotypes, and Chromosome Encodings
47 minutes
1.2
Objective & Fitness Function Design
10 minutes
1.3
Evolutionary Loop Architecture & Lifecycle
27 minutes
W2

Selection Mechanisms, Crossover & Mutation Operators

Gain hands-on experience with selection mechanisms, crossover & mutation operators.

3 videos34m
3 readings
3 topics
1 homework
Learn
W3

Multi-Objective Optimization & NSGA-II

Understand the architecture behind multi-objective optimization & nsga-ii.

3 videos59m
3 readings
3 topics
1 homework
Learn
W4

Advanced Evolutionary Paradigms (GP, NEAT & Co-evolution)

Implement production-grade advanced evolutionary paradigms (gp, neat & co-evolution).

3 videos34m
3 readings
3 topics
1 homework
Learn
01

Learn

Watch curated videos and read study resources

02

Practice

Practice what you learned

03

Build Projects

Build projects using your new gained knowledge

04

Submit & Verify

Submit your project and get verified by our system

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