Select a tag to browse associated projects and drill deeper into the tag cloud.
Evolving Objects
No analysis available
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
Mostly written in language not available
Licenses:
lgpl
Opt4J
Analyzed 4 months ago
Java framework for applying optimization algorithms like Evolutionary Algorithms, Particle Swarm Optimizers, or Simulated Annealing to arbitrary optimization problems.
Gemfony Optimization Library (Geneva)
G
Analyzed about 8 hours ago
Geneva is a library written in C++ for performing parametric optimization in parallel on devices ranging from multi-processor machines over clusters to Grids and Cloud installations. Geneva currently supports Evolutionary Algorithms, Swarm Algorithms, Gradient Descents, a form of Simulated Annealing
Mostly written in language not available
Licenses:
AGPL3
Java Genetic Algorithm Library
Analyzed 1 day ago
An object oriented library of an Genetic Algorithm, implemented in Java. Clear separation of the several concepts of the algorithm, e.g. Gene, Chromosome, Genotype, Phenotype, Population and Fitness Function. The fitness calculation is parallelized.
Licenses:
apache_2
coconet
Analyzed 1 day ago
Coconet is a program that generate and evolve feedfoward neural networks for classification problems.
Licenses:
gpl3_or_l...
MOEA Framework
Analyzed about 2 hours ago
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.
moses
M
Analyzed about 5 hours ago
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.
Licenses:
apache_2