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Analyzed 20 days ago. based on code collected 20 days ago.

Project Summary

Meta-optimizing semantic evolutionary search (MOSES) is a new approach to program evolution, based on representation-building and probabilistic modeling. MOSES has been successfully applied to solve hard problems in domains such as computational biology, sentiment evaluation, and agent control. Results tend to be more accurate, and require less objective function evaluations, in comparison to other program evolution systems. Best of all, the result of running MOSES is not a large nested structure or numerical vector, but a compact and comprehensible program written in a simple Lisp-like mini-language.

For more information see: http://metacog.org/doc.html.

Interested C++ developers, please drop in at #opencog on IRC.freenode.net.

Tags

artificialintelligence combinatorylogic evolutionaryalgorithm evolutionarycomputation evolutionaryprogramming geneticalgorithm geneticprogramming machinelearning particleswarmoptimization programevolution

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Project Security

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About Project Security

Languages

Languages?height=75&width=75
C++
89%
8 Other
11%

30 Day Summary

Jan 1 2020 — Jan 31 2020

12 Month Summary

Jan 31 2019 — Jan 31 2020
  • 24 Commits
    Down -51 (68%) from previous 12 months
  • 2 Contributors
    Down -5 (71%) from previous 12 months