Neural Network with Genetic Algorithms Experiment 1 (food and water)
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An experiment using Neural Networks and genetic algorithms I have always found you understand something better if you build it yourself.
My aim was to make a "lifeform" that learnt basic needs. The life form needs water and food to survive. Water is always available, food is scattered and once eater re-spawns elsewhere. If the "lifeform" runs out of either then it is classed as "dead"
There are 30 "lifeforms" in each generation, and each generation runs for a maximum of 6000 iterations. The project is setup with a Neural Network with 8 inputs, two hidden layers and 2 outputs.
With the current configuration and topology, by the 950th generation 2/3 of the lifeforms survive.
I have included data for the network after I left it running over night (it reached 17120 generations)
Source code at:
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Live demo at:
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