EO is a template-based, ANSI-C++ evolutionary computation library which helps you to write your own stochastic optimization algorithms insanely fast.
With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems
... [More], from continuous to combinatorial ones.
Designing an algorithm with EO consists in choosing what components you want to use for your specific needs, just as building a structure with Lego blocks. [Less]
OptaPlanner optimizes business resource usage. Every organization faces planning problems: provide products or services with a limited set of constrained resources (employees, assets, time and money). OptaPlanner optimizes such planning to do more business with less resources.
OptaPlanner is a
... [More] lightweight, embeddable planning engine written in Java™. It helps normal Java™ programmers solve constraint satisfaction problems efficiently. Under the hood, it combines optimization heuristics and metaheuristics with very efficient score calculation.
OptaPlanner is open source software, released under the Apache Software License. It is 100% pure Java™, runs on any JVM and is available in the Maven Central Repository too. [Less]
METSlib is an OO (Object Oriented) metaheuristics framework in C++.
Model and algorithms are modular: all the implemented search algorithms can be applied to the same model and personalized algorithms can be applied to very different models.
METSlib implements the basics of some metaheuristics
... [More] algorithm: Random Restart Local Search, Variable Neighborhood Search, Iterated Local Search, Simulated Annealing (with linear, exponential and custom cooling schedule), and last but not least Tabu Search. [Less]
The MOEA Framework is an open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose optimization algorithms and metaheuristics. A number of algorithms are provided out-of-the-box, including NSGA-II, ε-MOEA, GDE3 and MOEA/D.
... [More] In addition, third-party tools like JMetal and PISA directly integrate with the MOEA Framework.
The MOEA Framework targets an academic audience, providing the resources necessary to rapidly design, develop, execute and statistically test optimization algorithms. This includes over 40 test problems from the literature, and a suite of statistical tools for comparing and analyzing algorithm performance. [Less]
ECF is a C++ framework intended for application of any type of evolutionary computation. Current features include:
* parameterless: genotype (individual structure) is the only mandatory parameter
* genetic algorithm genotypes (bitstring, binary encoded real values, floating point vectors
... [More], permutation vectors), genetic programming genotype (tree)
* individuals may contain any genotypes in any number
* algorithms: steady state tournament, generational roulette-wheel, elimination, particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), clonal selection (CLONALG), genetic annealing, random search
* parallel execution in many models (global paralel EA, distributed EA, hybrid parallel EA...) using MPI [Less]
KaHyPar is a multilevel hypergraph partitioning framework for optimizing the cut- and the (λ − 1)-metric. It supports both recursive bisection and direct k-way partitioning. KaHyPar instantiates the multilevel approach in its most extreme version, removing only a single vertex in every level of the
... [More] hierarchy. By using this very fine grained n-level approach combined with strong local search heuristics, it computes solutions of very high quality. Its algorithms and detailed experimental results are presented in several research publications. [Less]