Search Results for "environment-modules"

OpenDino is an open source Java platform for optimization, design of experiment and learning. It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer

GAKNN is a data mining software for gene annotation data. GAKNN is built with k- Nearest Neighbour algorithm optimized by the genetic algorithm. Gene annotation datasets saved under .csv or .arff formats with Gene Ontology or FunCat categorization can use GAKNN to predict gene functions.

Evokit is an integrated environment for developing evolutionary computation algorithms. It is built around Java and the Eclipse platform, and will support several different libraries including ECJ.

Framework for development of simple evolutionary algorithms / island models programs in distributed environment using MapReduce programming model based on hadoop.

breve is a free, open-source 3D multi-agent simulation package designed for simulating decentralized systems and artificial life.

Evolve is a simulation written in java that uses the Genetic Algorithm to evolve intelligent agents acting in a rich environment.

A simulation environment for 2D robots to interact and perform tasks in. Meant to be educational and demonstrate emergent behaviors. No download necessary! See demos on project website!

The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.

The Airs GPT project experiments with Steady State Genetic Programming create agents that play tag. It emphasises co-evolution through competition, and provides the user with some tools to help them visualize what is taking place.