Search Results for "framework"

This is the official repository for the CUSTOMHyS-Qt software. CUSTOMHyS-Qt is a GUI for the CUSTOMHyS framework, which is an interactive tool for customysing heuristic-based algorithms. The CUSTOMHyS-Qt is written in Python and uses the PyQt5 library for the GUI. Further references about the backend can be found in the CUSTOMHyS repository.

ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners.

jMetal is an object-oriented Java-based framework for solving multi-objective optimization problems with metaheuristics.

Opt4J is an open source Java-based framework for evolutionary computation. It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary meta-heuristic optimization algorithms. ...

JCLEC is a general purpose evolutionary computation framework developed in Java. Some of its mains features are: multilevel architecture, highly reusable and integrable with other systems, easy to use and a lot of implemented algorithms and operation

Search Agents - a framework for collaboration of meta-heuristic agents

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

Sleepwalker aims to provide a highly abstract, universal, reusable, extensible Java-based genetic algorithms framework which can be used as a basis for modelling and programming virtually any practical optimisation problem.

X-GAT (XML-based Genetic Algorithm Toolkit) is a Java framework to optimize problems with Genetic Algorithms (GAs). Differently from other frameworks, X-GAT contains ready-to-use GAs implementations and new features can be easily added.

Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.

CILib is a framework for developing Computational Intelligence software in swarm intelligence, evolutionary computing, neural networks, artificial immune systems, fuzzy logic and robotics.

A .net implementation of a framework for genetic algorithms. This tool enables programmers to write the "core" of their problem and have a genetic algorithm immediately setup for solving it.

Very useful framework library, contains many frequent used code snippets, algorithms, data structures, WinForms & APS.NET controls for all-day work.

The Gene Expression Programming Framework in Java. It separates the process of evolution from the process of interpretation of the chromosome, allowing the use of various schemes in the chromosome.

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.

A framework for utilising the Genetic Algorithm in the domain of Game Theory

Galapagos is a Genetic Algorithm framework written in Java 5 with the intended audience of undergraduates in an Artificial Intelligence class. The goal of Galapagos is usability: a competent student should be able to learn this library in an afternoon.

Simple genetic programming framework. Features a robust set of interfaces and standard implementations for rapid development and ease of experimentation.

MAIF is developed in Java 5 (especially Generics) and aims at building AI algorithms, by concentrating onto the mapping of real-world problems, while abstracting from their inner working. It can be extended with new algorithms and problem representations.

A java framework for developing meta-heuristics that supports the use of grids environments. The meta-heuristics planned to be realeased are GAs, VNS and NNs. The grid middleware that will be firstly explored is the OurGrid solution.

This is a cross-platform framework for using Genetic Algorithms for solutions. Written in Java and uses convinient plug-in features for every phase in the genetic development, while maintaining an easy-to-use API for easy integration into applications.