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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

Vulnerabilities per Version ( last 10 releases )

There are no reported vulnerabilities

Project Vulnerability Report

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About Project Security

Languages

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

30 Day Summary

Jan 28 2021 — Feb 27 2021

12 Month Summary

Feb 27 2020 — Feb 27 2021
  • 20 Commits
    Down -4 (16%) from previous 12 months
  • 3 Contributors
    Up + 1 (50%) from previous 12 months