Meta-Optimizing Semantic Evolutionary Search
From OpenCog
See also MOSES: the Pleasure Algorithm and OpenCogPrime:ProbabilisticEvolutionaryLearningOverview.
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.
More at: http://metacog.org/doc.html.
C++ code at: https://code.launchpad.net/opencog (opencog/learning/moses)
C++ code at: http://code.google.com/p/moses/ (earlier versions)
LISP code at: http://code.google.com/p/plop/
