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Life & fun · June 24, 2015

MF

My first Genetic Algorithm program

Wrote a program in FreeBASIC to try and find out how Genetic Algorithms work. It's a quick hack that I wrote to try some theories with because the Wikipedia Article on GAs reads like greek to me. So I built it from the general principles i knew at the time. The "genome" per individual is an array of 9000 integers but I used only monochrome pictures with either black or white; An individual also has a fitness score. There are 1000 individuals in the population and only two are selected to reproduce. So that makes 9 million (yes, MEELION as El Reg would write) data points and the scoring…

My first Genetic Algorithm program launched on June 24, 2015 with 19 votes, #1,364 of 3,954 launches that month and more than 75% of that year's launches.

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In plain words

This is a FreeBASIC program that demonstrates how genetic algorithms work through practical experimentation. It evolves a population of 1,000 individuals, each representing a 9,000-integer genome that encodes monochrome images, by selecting the two fittest candidates to reproduce each generation. The program tracks fitness scores across millions of data points and applies a 0.002% mutation rate to prevent stalling. Built as an educational exploration rather than an optimized tool, it's designed for people trying to understand genetic algorithms through hands-on implementation.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

Wrote a program in FreeBASIC to try and find out how Genetic Algorithms work. It's a quick hack that I wrote to try some theories with because the Wikipedia Article on GAs reads like greek to me. So I built it from the general principles i knew at the time. The "genome" per individual is an array of 9000 integers but…

Does a similar job

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    Everyone saw the AlphaEvolve hype. I got obsessed with how it might work under the hood and decided to just build it myself. My setup uses GPT-4.1 to mutate matrix multiplication code, guided by a bunch of hand-crafted mutation strategies (loop reordering, tiling, Strassen, etc.). Each candidate is evaluated on both speed and accuracy. Then I apply Pareto selection with crowding distance to evolve better ones over generations. I ran into all the usual LLM reward hacks-returning the input, calling np.dot, etc. So I forced primitive-only implementations and tightly constrained the mutation…

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    Everything that would be here is in the README. I hope this gets big, it has tons of potential.

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