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Optimizes term lists for regex using a Trie

Optimizes database performances by removing old and useless data.

Drools Planner optimizes automated planning by combining metaheuristic search algorithms with rule engine powered score calculation. This is the drools planner multiproject.

Drools Planner optimizes automated planning by combining metaheuristic search algorithms with rule engine powered score calculation. This is the drools-planner-webexamples module which contains web examples on how to use Drools Planner in a war file.

Drools Planner optimizes automated planning by combining metaheuristic search algorithms with rule engine powered score calculation. This is the drools-planner-examples module which contains examples on how to use Drools Planner.

Drools Planner optimizes automated planning by combining metaheuristic search algorithms with rule engine powered score calculation. This is the drools-planner-benchmark module which contains the benchmarker tool.

Drools Planner optimizes automated planning by combining metaheuristic search algorithms with rule engine powered score calculation. This is the drools-planner-core module which contains metaheuristic algorithms.

ProGuard is a free Java class file shrinker, optimizer, and obfuscator. It removes unused classes, fields, methods, and attributes. It then optimizes the bytecode. It then renames the remaining classes, fields, and methods using short meaningless names. Finally, it preverifies the processed code for Java 6 or for Java Micro Edition.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.