Evolutionary computation is popular in the holidays

It seems to me that so-called exotic computer science topics become popular each holiday season, at least in the context of the news sources I consume. I eluded to this in a recent post, in particular computational intelligence. Now it seems the trend is evolutionary computation. Some highlights that have cropped up recently include:

• Genetic algorithm for building a car: A flash program that begins execution on page load toward evolving a two-wheeled car in a two-dimensional landscape. The objective function appears to based on keeping the red circles from touching the ground, time, and perhaps distance travelled.
  
• Genetic Programming: Evolution of Mona Lisa: Example by Roger Alsing of using genetic programming to evolve a set of 50 polygons to represent a source image, specifically the Mona Lisa. A FAQ is provided that comments that the objective function is the sum error between the generated image and the source image, meaning the result is an approximation of the source.
  
• Image Evolution: A web application that uses simulated annealing to optimize the color, number of vertices, and orientation of a set of 50 polygons on an HTML canvas element to represent a given image, the default of which is Mona Lisa. Inspired by the popularity of the above approach.
• Statistics vs. Machine Learning, fight! A great overview comparing and contrasting machine learning and statistics. This argument crops up every year or so, some good points though.
• Application of Genetic Programming to the "Snake Game": The resurgence of this 2000 tutorial on using genetic programming to evolve a controller to successfully play snake.
  

Give the theme, there have been a number of recent requests for good tutorials, frameworks, and resources for learning about evolutionary computation and machine learning. Most are pretty crap, I'm sure this represents an opportunity, now that sites like ai-depot and generation5 are defunct.