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mind:atlas-evolutionary-investigation-1 [2016/05/02 22:24]
martindm
mind:atlas-evolutionary-investigation-1 [2016/05/02 22:27]
martindm [Conclusions]
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 Using deliberate domain-specific mutations, we can achieve extremely efficient exploration of a space on simple tasks. If we can break down flow generation into a series of these simple tasks, then we can do flow generation quite cheaply. Using deliberate domain-specific mutations, we can achieve extremely efficient exploration of a space on simple tasks. If we can break down flow generation into a series of these simple tasks, then we can do flow generation quite cheaply.
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 +I need to do experiments on a full flowMap involving global properties such as branching factor, connections,​ segment length, angle, etc. One of the tricky parts of evolutionary algorithms is selecting a fitness function which is reasonably cheap to compute. Global fitness functions tend to be quite expensive on complex domains. However, I think I can mitigate this by using global properties which can be computed on-line (that is, they don't need to be re-evaluated from scratch. Each small change can incrementally update the properties listed above).
mind/atlas-evolutionary-investigation-1.txt ยท Last modified: 2016/05/02 22:27 by martindm
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