Search Results for "differential evolution"

...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

popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known network flow and other graph algorithms. A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. ...

Implementation of Point Symmetry-based Automatic Clustering Approach Using Differential Evolution Using bug fixed KD tree nearest neighbor search from https://github.com/CristianDallos/kmeansclustering. (It is also modified to search for multiple nearest points instead only for one.) Used those academic works for algorithm implementation: http://cs.cug.edu.cn/teacherweb/gwy/Publication/ISICA-09.pdf http://www.isical.ac.in/~sanghami/ieeetkde_cameraready.pdf http://www.softcomputing.net/smca-paper1.pdf 1. ...

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. For this purpose, Opt4J relies on a module-based implementation and offers a graphical user interface for the configuration as well as a visualization of the optimization process.

We propose the use of Differential Evolution algorithm for the weight adjustment of base classifiers used in weighted voting heterogeneous ensemble of classifier. Average Matthews Correlation Coefficient (MCC) score, calculated over 10-fold cross-validation, has been used as the measure of quality of an ensemble. DE/rand/1/bin algorithm has been utilised to maximize the average MCC score calculated using 10-fold cross-validation on training dataset.

The MOEA Framework is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose multiobjective optimization algorithms. The MOEA Framework supports genetic algorithms, differential evolution, particle swarm optimization, genetic programming, grammatical evolution, and more. A number of algorithms are provided out-of-the-box, including NSGA-II, NSGA-III, ε-MOEA, GDE3 and MOEA/D. In addition, the MOEA Framework provides the tools necessary to rapidly design, develop, execute and statistically test optimization algorithms.

This is a GUI, Java implementation of the Ant Colony Optimisation/Particle Swarm Optimisation (PSO/ACO2) rule induction algorithm. This project was inspired by Ant-Miner, but handles continuous attributes using PSO or now Differential Evolution.