Copyright 2005-2007, Moshe Looks and Novamente LLC

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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Overview
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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 .

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The Code
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The code for reproducing all of the published experiments on MOSES (see below)
may be found in the moses/ subdirectory. A more nicely architected and
performant implementation that is under active development may be found in the
moses2/ subdirectory. These two directories are completely independent and use
no common files (although identical versions of some files may be found in both
subdirectories).

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Installation
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Go into either moses/ or moses2/ (see above) and do 'make'. You will need to
have a recent gcc (4.x or late 3.x) and the boost libraries installed
(http://www.boost.org).

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Publications on MOSES
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1. Moshe Looks, "Scalable Estimation-of-Distribution Program Evolution",
Genetic and Evolutionary Computation COnference (GECCO), 2007.

2. Moshe Looks, "On the Behavioral Diversity of Random Programs", Genetic and
Evolutionary Computation COnference (GECCO), 2007.

3. Moshe Looks, "Meta-Optimizing Semantic Evolutionary Search", Genetic and
Evolutionary Computation COnference (GECCO), 2007.

4. Moshe Looks, Ben Goertzel, Lucio de Souza Coelho, Mauricio Mudado, and
Cassio Pennachin,"Clustering Gene Expression Data via Mining Ensembles of
Classification Rules Evolved Using MOSES", Genetic and Evolutionary
Computation COnference (GECCO), 2007.

5. Moshe Looks, Ben Goertzel, Lucio de Souza Coelho, Mauricio Mudado, and
Cassio Pennachin, "Understanding Microarray Data through Applying Competent
Program Evolution", Genetic and Evolutionary Computation COnference (GECCO),
2007.

6. Moshe Looks, "Competent Program Evolution" Doctoral Dissertation, Washington
University in St. Louis, 2006.

7. Moshe Looks, "Program Evolution for General Intelligence", Artificial
General Intelligence Research Institute Workshop (AGIRI), 2006.