Biased generative art

This project is focussed on adding a twist to the generative art algorithm for NFT projects. With 10k generative art projects, the rarity of an NFT is left to straight random number generation. This model is great if you don't care about the rarity of certain attributes. The reason I created this project is because there are many real world scenarios of scarcity. Due to human interfernce, more and more animals around the world are at risk of going extinct. For this hackathon, I am looking to model real world bee populations as NFTs. There are over 20k bee species some of which are: honeybees, bumble bees, carpenter bees, digger bees, leaf cutter bees, cuckoo bees and more. And there are many that are going extinct. The attributes of endangered bees will have lower probability of being picked and minted than those of common bee species.

For the MVP, I drew up 6 bee species, 3 common and 3 endagered. Each have a probability associated with it that is meant to represent their real world population size compared to it's historical population density before being considered endangered. The algorithm is simple. Take the probabilities in an array, generate a random number, then compare random number against the sum of probabilites one at a time. To verify each bee was chosen close to its probability weight, I ran the test multiple times at 1k, 10k, 100k, & 1M iterations. Each time, the algorithm chose the individual species with great accuracy rtw to their probability. After the algorithm and the drawings, I minted the images through NFTPort and created an auction house through Zora. This is to show case the process of selection, minting, and selling.