Merge pull request #377 from nature-of-code/notion-update-docs

Editing more of Chapter 10

content/06_libraries.html

@@ -835,8 +835,8 @@ <h3 id="exercise-66">Exercise 6.6</h3>
   </figure>
   <p>Create a vehicle that has revolute joints for its wheels. Consider the size and positioning of the wheels. How does changing the <code>stiffness</code> property affect their movement?</p>
   <figure>
-    <img src="images/06_libraries/06_libraries_13.png" alt=" ">
-    <figcaption> </figcaption>
+    <img src="images/06_libraries/06_libraries_13.png" alt="">
+    <figcaption></figcaption>
   </figure>
 </div>
 <h3 id="mouse-constraints">Mouse Constraints</h3>

content/09_ga.html

@@ -237,7 +237,6 @@ <h3 id="b-create-a-mating-pool">B) Create a mating pool.</h3>
   <figcaption>Figure 9.2: A “wheel of fortune” where each slice of the wheel is sized according to a fitness value.</figcaption>
 </figure>
 <p></p>
-<p></p>
 <p>Spin the wheel and you’ll notice that Element B has the highest chance of being selected, followed by A, then E, then D, and finally C. This probability-based selection according to fitness is an excellent approach. One, it guarantees that the highest-scoring elements will be most likely to reproduce. Two, it does not entirely eliminate any variation from the population. Unlike with the elitist method, even the lowest-scoring element (in this case C) has a chance to pass its information down to the next generation. It’s quite possible (and often the case) that even low-scoring elements have a tiny nugget of genetic code that is truly useful and should not entirely be eliminated from the population. For example, in the case of evolving “to be or not to be”, we might have the following elements.</p>
 <table>
   <tbody>