Search Results for "algorithms framework"

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.

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. ...

HH-Evolver is a framework for domain-specific, hyper-heuristic evolution. HH-Evolver automates the design of domain-specific heuristics for planning domains. Hyper-heuristics generated by our tool can then be used with combinatorial search algorithms such as A* and IDA* for solving problems of the given domain.

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.

The Neo A.I Framework is a group of technologies and tools used to create A.I like software or software with A.I integrated into it

GEP is an evolutionary algorithm for function finding. This framework is a powerful way of expressing and coding genetic-like structures and quickly finding solutions through evolution by common genetic operators.

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.

a framework library with a template method interface and inheritable classes for an executable process design which produces an executable which is both a GUI and a console application. It provides multi-threading for work items of the provider process .

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.

CuberGA project is the flexible Genetic Algorithms framework. Realize your ideas easy with deliveries of this project. Keywords: genetic algorithms, framework, permutation, mutation, crossover, genotype, selection, survival, Левченко Илья.

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.