Re-written university coursework from my MSc Artificial Intelligence
Genetic Algorithms are heuristic computer simulations that find parameter configurations in complex search spaces by modelling evolution by artificial selection. Cooperative co-evolution was adapted from nature in 1994 to improve the speed of finding a solution of genetic algorithms by maintaining sub populations with competing specialised individuals. This work shows that the original paper, claiming that co-evolution speeds up the problem, appears to be mainly faster because it decomposes the problem. A modular genetic algorithm is presented that engineers a genome from multiple parents and has the same performance as its co-evolutionary counterpart without maintaining sub populations on the original fitness landscapes.
Check out this post describing the background, methods, and discussion. The code is found here.
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