Projects

Evolving Objects

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]

Opt4J

Java framework for applying optimization algorithms like Evolutionary Algorithms, Particle Swarm Optimizers, or Simulated Annealing to arbitrary optimization problems.

Python Pyevolve

Pyevolve was developed to be a complete genetic algorithm framework written in pure python.

dANN

The dANN project is a library to help facilitate artificial neural networks within other applications. It is currently written in Java, C++, and C#. However only the java version is currently in active development. If you want to obtain a version other than the java version you will need to get it ... [More] directly from GIT. Our intentions are two fold. First, to provide a powerful interface for programs to include conventional artificial neural network technology into their code. Second, To act as a testing ground for research and development of new AI concepts. We provide new AI technology we have developed, and the latest algorithms already on the market. In the spirit of modular programming the library also provides access to the primitive components giving you greater control. [Less]

EpochX

EpochX is a genetic programming framework for Java. It is designed specifically for the task of analyzing evolutionary automatic programming, so is ideal for researchers who require an extendable system for studying the effects of new operators or procedures. EpochX supports 3 popular ... [More] representations - Strongly-Typed tree GP - Context-Free Grammar GP - Grammatical Evolution [Less]

vita

Vita is a scalable, high performance C++ environment / toolkit for classification, symbolic regression, content base images retrieval, data mining and agent control.

deap

DEAP is intended to be an easy to use distributed evolutionary algorithm library in the Python language. Its two main components are modular and can be used separately. The first module is a Distributed Task Manager (DTM), which is intended to run on cluster of computers. The second part is the Evolutionary Algorithms in Python (EAP) framework.

pyevolve

PyevolveDescriptionThis project focus on development of a complete cross-platform (Windows, Linux) framework for Genetic Algorithms in pure python. The project is in active development, and I'm doing great efforts to release a good version and documentation soon. Main features: Many selector ... [More] algorithms like: Rank Selector Uniform Selector Roulette Wheel Selector Tournament Selector Pure python, you can use in any platform which runs python; Easy to use, easy for end-user; Simple to customize Create new representations, genetic operators; You can view the Tutorial - Christian S. Perone News 05/08/2008 Added documentation to the code; Created API Docs. 04/08/2008 I've created a Tutorial. 31/07/2008 Released Pyevolve 0.3 (changes in ChangeLogVersion03)! 29/06/2008 Good news, Pyevolve was figured in the "10 Must-Have Python Packages for Social Scientists" ! 25/01/2008 Released Pyevolve 0.2 (changes in ChangeLogVersion02)! Some code example (SimpleCodeExample)from pyevolve import G1DList from pyevolve import GSimpleGA from pyevolve import Selectors def eval_func(ind): score = 0.0 for x in range(0,len(ind)): if ind[x]==0: score += 0.1 return score # Genome instance genome = G1DList.G1DList(7) genome.setInitParams(rangemin=0, rangemax=6) # The evaluator function (objective function) genome.evaluator.set(eval_func) # Genetic Algorithm Instance ga = GSimpleGA.GSimpleGA(genome) ga.selector.set(Selectors.GRouletteWheel) # Do the evolution ga.evolve() # Best individual print ga.bestIndividual() And the result: - GenomeBase Evaluated: True Score: 0.70 Fitness: 0.70 Slot name: Evaluator (Count: 1) Name: eval_func Doc: No documentation ! Slot name: Initializator (Count: 1) Name: G1DListInitializatorInteger Doc: This is the init function !! Slot name: Mutator (Count: 1) Name: G1DListMutatorSwap Doc: No documentation ! Slot name: Crossover (Count: 1) Name: G1DListCrossoverSinglePoint Doc: No documentation ! - G1DList List size: 7 List: [0, 0, 0, 0, 0, 0, 0] [Less]

moses

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. ... [More] 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. Interested C++ developers, please drop in at #opencog on IRC.freenode.net. [Less]

gene-centered-ecosystem-framework-gp

OCamL, Genetic Programming (news: currently porting to F# for easier GUI production) Abstraction-Based Genetic Programming (ABGP)ABGP is a genetic programming system in which the genotype search space is partitioned by the proofs to which each program is linked to via the Curry-Howard isomorphism. ... [More] The proofs act as species for organsims by specifying a pattern in which typed genes can be plugged in. Organisms are arrangements of gene as specified by the species to which they belong. Each gene is a closed computational block that may be combined with other blocks to form an organism. See the Context wiki page [Less]