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TL;DR In this article, we'll train the car to do self-parking using a genetic algorithm. We'll create the 1st generation of cars with random genomes that will behave something like this: On the ≈40th generation the cars start learning what the self-parking is and start getting closer to the parking spot: Another example with a bit more challenging starting point: Yeah-yeah, the cars are hitting so
TL;DR In this article I’m trying to explain the difference/similarities between dynamic programing and divide and conquer approaches based on two examples: binary search and minimum edit distance (Levenshtein distance). Also, in the Content-aware image resizing in JavaScript article I went through another powerful but yet simple example of dynamic programming for the Seam Carving algorithm. You mi
Content-aware image resizer based on Seam Carving algorithm
More examples Here are some more examples of how the algorithm copes with more complex backgrounds. Mountains on the background are being shrunk smoothly without visible seams. The same goes for the ocean waves. The algorithm preserved the wave structure without distorting the surfers. We need to keep in mind that the Seam Carving algorithm is not a silver bullet, and it may fail to resize the ima
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