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Gazoo is a Java framework for genetic algorithms development. Gazoo provides the core of a genetic algorithm, leaving to the user the implementation of specific-problem classes.
Solving the travelling salesman problem with genetic (evolutionary) algorithms. The distance calculations are based on geographical coordinates. The progress of the algorithm is visualized with a geo-map and some statistics.
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The Athlete is a virtual robot living in the ODE (Open Dynamics Engine)world. An evolution algorithm is implemented based on the DR-EA-M to optimize its performances. This involves a Genetic algorithm, network of neurons and morphology. All is in java.
Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
Domigene is a genetic algorithm API attempting to incorporate dominant and recessive genes. Supports modular evolution types, genetic programs, definable instruction sets, variation in population, chromosomes, mortality, mutation, fitness functions.
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aVolve is an evolutionary/genetic algorithm designed to evolve single-cell organisms in a micro ecosystem. It currently uses the JGAP Genetic algorithm, but does include a primitive genetic algorithm written in Python.
GAZES - Genetic Algorithm Zombie Eradication Simulation Grid-based simulator for zombie scenario. The units will use genetic algorithm to determine their actions on a cycle by cycle basis. This is a school project, so is likely on hiatus for summer.
NXTAI's main objective is to create a new generation of natural language processing program by using a unique neural networking model. To demonstrate the algorithm works, A chatbot will be developed as a classical example of how it can be used.
A simulator for self-organizing video replication and more
... algorithm, which lets the content place itself where it is needed. It is robust, scalable and adaptive. The transport path of content is exploited to replicate content and limited storage space is handled by clean-up mechanisms.
We implemented a simulator to show the algorithm's performance and to test its behavior under real circumstances. It is easily extendable to support several new scenarios or changes to the algorithm (see replication and client models).