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Evolving Objects

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  Analyzed 2 months ago

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]

60.7K lines of code

0 current contributors

over 5 years since last commit

6 users on Open Hub

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5.0
 
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Opt4J

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  Analyzed almost 2 years ago

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

53.4K lines of code

3 current contributors

almost 2 years since last commit

4 users on Open Hub

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0.0
 
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Python Pyevolve

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  Analyzed 3 months ago

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

0 lines of code

0 current contributors

0 since last commit

2 users on Open Hub

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5.0
 
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Mostly written in language not available
Licenses: Python 2.1.1

EpochX

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  Analyzed 6 months ago

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]

42.6K lines of code

0 current contributors

over 7 years since last commit

2 users on Open Hub

Activity Not Available
5.0
 
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dANN

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  Analyzed about 2 years ago

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]

8.32K lines of code

1 current contributors

almost 3 years since last commit

2 users on Open Hub

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5.0
 
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vita

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  Analyzed over 1 year ago

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.

19.3K lines of code

1 current contributors

over 1 year since last commit

1 users on Open Hub

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0.0
 
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deap

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  Analyzed 3 days ago

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]

10.4K lines of code

14 current contributors

7 days since last commit

1 users on Open Hub

Low Activity
0.0
 
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swarml

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  No analysis available

Soon I will release some interesting algorithms from the literature. But not today.

0 lines of code

0 current contributors

0 since last commit

0 users on Open Hub

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0.0
 
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Mostly written in language not available
Licenses: GPL-2.0+

evolvestuff

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  No analysis available

EvolveStuff is a genetic algorithm framework built in C# with example implementations. It's a concrete implementation behind an extensible library that allows you to develop the minimum amount of code to get a working GA evolving. Included are working examples of a classic 16 bit scenario ... [More] , checkers, and soon to be completed cribbage. Contact jake@developstuff.com if you want to talk GA and see how things are working. [Less]

0 lines of code

0 current contributors

0 since last commit

0 users on Open Hub

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0.0
 
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Mostly written in language not available
Licenses: MIT

genetik

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  Analyzed almost 2 years ago

GenetiK (Genetic Kernel)GenetiK is a generic framework that supports evolutionary algorithms. According to the evolutionary approach, solutions to particular problems can be represented as individuals of a population. For each individual, a particular function, called ''fitness'', can be defined ... [More] to associate a high fitness scores with 'good' solutions. Starting from any set of individuals, evolving this population (with a process that loosely resembles darwinian selection in biology) results, after a sufficient number of ''generations'', in a new population of high-fitness individuals. In other words, starting from any set (even randomly generated) of potential solutions to a problem, it is possible to obtain new sets of progressively 'better' solutions, eventually resulting in the selection of an optimal (or near-optimal) solution. Therefore, depending on the nature of the problem to be solved, it is possible to find a good solution by expressing candidate solutions and their fitness in an appropriate way. Even adopting the same evolutionary algorithm, but two different representations of individuals and fitness, can lead to dramatically different results, therefore finding a good representation of individuals and their fitness is crucial to the success or failure of a particular application. The idea behind the GenetiK project is to let users experiment with evolutionary computation without having to code from scratch the algorithm that deals with the evolution itself, but rather let them concentrate on their specific tasks and objectives. This allows users to focus on finding good representations for candidate solution, the best ways to measure fitness and other aspects, but, at the same time, allowing them to fine-tune the algorithm to their specific needs by customizing its behaviour, if required. Project StructureThe GenetiK framework is divided in 5 subprojects: genetiK -- that includes the common base classes genetiK::ga -- specialized in dealing with Genetic Algorithms genetiK::gp -- oriented towards Genetic Programming genetiK::gp::st -- dedicated to Strongly Typed Genetic Programming genetiK::util -- includes all the utility classes Here you can find an UML diagram describing the structure of the framework. (Source XMI file, generated with Umbrello) genetiK provides the common evolutionary algorithms functionalities, while genetiK::ga, genetiK::gp and genetiK::gp::st contains the specific classes that should be extended to develop a specific solution. You can consult the annotated example (the genetic algorithm that comes with the library) to see how the framework can be used. DocumentationPlease refer to HTML documentation for more information. Getting the codeTo check out the current version of GenetiK use svn checkout http://genetik.googlecode.com/svn/trunk/ genetikand compile with make(We hope that your current platform is supported ) Running the exampleAfter compiling the library, you can run the example by typing ./bin/example [Less]

39.1K lines of code

0 current contributors

almost 9 years since last commit

0 users on Open Hub

Activity Not Available
0.0
 
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