PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
PyBrain is short for Python-Based Reinforcement Learning
... [More], Artificial Intelligence and Neural Network Library.
It's the Swiss army knife for machine learning and neural networking. [Less]
EO is a template-based, ANSI-C++ evolutionary computation library which helps you to write your own stochastic optimization algorithms insanely fast.
With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems
... [More], from continuous to combinatorial ones.
Designing an algorithm with EO consists in choosing what components you want to use for your specific needs, just as building a structure with Lego blocks. [Less]
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelization mechanism such as multiprocessing and SCOOP. The following documentation presents the
... [More] key concepts and many features to build your own evolutions. [Less]
Vita is a scalable, high performance C++ genetic programming / genetic algorithms framework.
It's suitable for classification, symbolic regression, content base image retrieval, data mining and agent control.
The MOEA Framework is an open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose optimization algorithms and metaheuristics. A number of algorithms are provided out-of-the-box, including NSGA-II, ε-MOEA, GDE3 and MOEA/D.
... [More] In addition, third-party tools like JMetal and PISA directly integrate with the MOEA Framework.
The MOEA Framework targets an academic audience, providing the resources necessary to rapidly design, develop, execute and statistically test optimization algorithms. This includes over 40 test problems from the literature, and a suite of statistical tools for comparing and analyzing algorithm performance. [Less]
Learn Finite State Automata that generalize over a set of training instances.
This is an (incomplete) implementation of GAL, the Genetic Automaton Learner as
defined in Chapter 5 of Belz (2000).
The point of this code is to learn a FSA that *generalizes* from a set of positive
examples. (If
... [More] you just want to cover exactly the input, you can use any existing
package for doing FSA minimization.)
The input file is in text format, one sequence per line, the alphabet will be induced
by tokens separated by white-spaces. [Less]
An efficient, stable research evolutionary computation and genetic programming research toolkit written in Java. Download at the ECJ Home Page, not here. This is an old repository. For the latest CVS repository, go to the ECJ repository at java.dev
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