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Low-poly image generation using evolutionary algorithms in Ruby (2023)


Inspired by biological systems, evolutionary algorithms model the patterns of multi-generational evolution in order to unearth unique ideas. They work by generating a vast number of potential solutions to a particular problem and then pitting them against each other in a process akin natural selection: only the fittest survive. In this way, evolutionary algorithms are able to navigate large ambiguous search spaces in order to find solutions to problems that may be difficult or inefficient to solve using other methods.

In this way, evolutionary algorithms are able to navigate large ambiguous search spaces in order to find solutions to problems that may be difficult or inefficient to solve using other methods. These algorithms are used for a wide variety of tasks: from optimizing neural network parameters, evolving mechanical structures, simulating protein folding, and even generating art! In the case of image reconstruction and other generative tasks, a random initialization may be better because it allows the algorithm to explore the search space more creatively, though it may take longer to converge on a solution.

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