21 BOM

lessons/6-Other/21-GeneticAlgorithms/README.md

@@ -2,14 +2,12 @@
 
 ## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/121)
 
-**Genetic Algorithms** (GA) are based on **evolutionary approach** to AI, in which methods of evolution of population is used to obtain an optimal solution for a given problem. They were proposed in 1975 by [John Henry Holland](https://en.wikipedia.org/wiki/John_Henry_Holland).
-
-> **Watch** [this great video](https://www.youtube.com/watch?v=qv6UVOQ0F44) talking about how computer can learn to play Super Mario using neural networks trained by genetic algorithms. We will learn more about computer learning to play games like that [at the next section](../22-DeepRL/README.md).
+**Genetic Algorithms** (GA) are based on an **evolutionary approach** to AI, in which methods of the evolution of a population is used to obtain an optimal solution for a given problem. They were proposed in 1975 by [John Henry Holland](https://wikipedia.org/wiki/John_Henry_Holland).
 
 Genetic Algorithms are based on the following ideas:
 
 * Valid solutions to the problem can be represented as **genes**
-* **Crossover** allows us to combine two solutions together to obtain new valid solution
+* **Crossover** allows us to combine two solutions together to obtain a new valid solution
 * **Selection** is used to select more optimal solutions using some **fitness function**
 * **Mutations** are introduced to destabilize optimization and get us out of the local minimum
 
@@ -19,40 +17,57 @@ If you want to implement a Genetic Algorithm, you need the following:
  * On the set of genes Γ we need to define **fitness function** fit: Γ→**R**. Smaller function values correspond to better solutions.
  * To define **crossover** mechanism to combine two genes together to get a new valid solution crossover: &Gamma;<sup>2</sub>&rightarrow;&Gamma;.
  * To define **mutation** mechanism mutate: &Gamma;&rightarrow;&Gamma;.
+
 In many cases, crossover and mutation are quite simple algorithms to manipulate genes as numeric sequences or bit vectors.
 
-Specific implementation of a genetic algorithm can vary from case to case, but overall structure is the following:
+The specific implementation of a genetic algorithm can vary from case to case, but the overall structure is the following:
 
-1. Select initial population G&subset;&Gamma;
+1. Select an initial population G&subset;&Gamma;
 2. Randomly select one of the operations that will be performed at this step: crossover or mutation
 3. **Crossover**:
   * Randomly select two genes g<sub>1</sub>, g<sub>2</sub> &in; G
   * Compute crossover g=crossover(g<sub>1</sub>,g<sub>2</sub>)
   * If fit(g)<fit(g<sub>1</sub>) or fit(g)<fit(g<sub>2</sub>) - replace corresponding gene in the population by g.
 4. **Mutation** - select random gene g&in;G and replace it by mutate(g)
-5. Repeat from step 2, until we get sufficiently small value of fit, or until the limit on the number of steps is reached.
+5. Repeat from step 2, until we get a sufficiently small value of fit, or until the limit on the number of steps is reached.
 
 ## Typical Tasks
 
-Tasks typically solved by GA:
+Tasks typically solved by Genetic Algorithms include:
 
 1. Schedule optimization
 1. Optimal packing
 1. Optimal cutting
 1. Speeding up exhaustive search
 
-## Notebooks
+## ✍️ Exercises: Genetic Algorithms
 
-Go to [Genetic.ipynb](Genetic.ipynb) notebooks to see two examples of using Genetic Algorithms:
+Continue your learning in the following notebooks:
+
+Go to [this notebook](Genetic.ipynb) to see two examples of using Genetic Algorithms:
 
 1. Fair division of treasure
-1. 8 Queen Problem
+1. 8 Queens Problem
+
+## Conclusion
+
+Genetic Algorithms are used to solve many problems, including logistics and search problems. The field is Inspired by research that merged topics in Psychology and Computer Science. 
+
+## 🚀 Challenge
+
+"Genetic algorithms are simple to implement, but their behavior is difficult to understand." [source](https://wikipedia.org/wiki/Genetic_algorithm) Do some research to find an implementation of a genetic algorithm such as solving a Sudoku puzzle, and explain how it works as a sketch or flowchart.
 
 ## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/221)
 
-## Assignment
+## Review & Self Study
 
-Your goal is to solve so-called **Diophantine equation** - an equation with integer roots. For example, consider the equation a+2b+3c+4d=30. You need to find integer roots that satisfy this equation.
+Watch [this great video](https://www.youtube.com/watch?v=qv6UVOQ0F44) talking about how computer can learn to play Super Mario using neural networks trained by genetic algorithms. We will learn more about computer learning to play games like that [in the next section](../22-DeepRL/README.md).
+
+## [Assignment: Diophantine Equation](Diophantine.ipynb)
+
+Your goal is to solve so-called **Diophantine equation** - an equation with integer roots. For example, consider the equation a+2b+3c+4d=30. You need to find the integer roots that satisfy this equation.
+
+*This assignment is inspired by [this post](https://habr.com/post/128704/).*
 
 Hints:
 
@@ -60,7 +75,3 @@ Hints:
 1. As a gene, consider using the list of root values
 
 Use [Diophantine.ipynb](Diophantine.ipynb) as a starting point.
-
-*This assignment is inspired by [this post](https://habr.com/post/128704/).*
-
-> ✅ Todo: conclusion, challenge, reference.