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]
ParadisEO is a C++ white-box object-oriented software framework for to the reusable design of metaheuristics.
* Portable on: Windows, Unix and MacOS
* Parallel and distributed architectures (MPI)
* Grids (Globus, Condor-G/MW)
It is composed of 4 interconnected modules:
... [More] population-based metaheuristics (evolutionary algorithms, particle swarm optimization, evolution strategy, differential evolution...)
* ParadisEO-MO for solution-based metaheuristics (local search, simulated annealing, tabu search, iterated local search, variable neighborhood search...)
* ParadisEO-MOEO for multiobjective optimization
* ParadisEO-PEO for hybrid, parallel and distributed metaheuristics
* ParadisEO-SMP for memory-shared parrallel metaheuristics [Less]
Open Metaheuristic (oMetah) is a library aimed at the conception of metaheuristics (i.e. genetic/evolutionnary algorithms, tabu search, simulated annealing, ant colony algorithms, etc.). It follows the "adaptive learning search" approach in the design of metaheuristics (an approach inspired from the
... [More] "adaptive memory programming").
One of the main goal of oMetah is to permit rigourous empirical tests of metaheuristics, through a statistical approach. [Less]
Sci-Wi is a free software for the management of scientific peer-reviewing.
Its goal is to manage a web site gathering reviews of research articles, already published online elsewhere. The reviews are made by mandated researchers.
A stochastic method for planning decomposition, called Divide-and-Evolve, that was introduced recently and which focuses on plan quality. The basic principle is to search the space of state decompositions of the planning problem at hand by means of artificial evolution: candidate solutions are
... [More] sequences of intermediate goals which define consecutive planning subproblems that are hopefully easier to solve than the global problem. The scope of Divide-and-Evolve is temporal planning as defined by PDDL2.1 (the widely adopted planning domain description language standard) where the problems are described using durative actions and where plan quality is the total makespan. [Less]