Some of the available frameworks are:



Customizing optimization metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but is not limited to, continuous optimization problems using a hyper-heuristic approach for customizing metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature.


MatHH is a Matlab-based framework to allow rapid prototyping of Hyper-Heuristics (HHs). The reason for creating this framework is that HHs have proven to be a valuable tool for solving complex problems, such as Combinatorial Optimization Problems (COPs). These solvers have an assorted set of models arising from extensive scientific research. It is also customary that researchers develop their models from scratch, which increases development times. In turn, this makes the drafting and testing of new ideas burdensome and time-consuming. MatHH aims to speed up the development of new models by integrating a modular approach and a straightforward programming language.


EvoHyp provides the following packages for evolutionary algorithm hyper-heuristics, such as

  • GenAlg – A genetic algorithm hyper-heuristic for selection hyper-heuristics
  • DistrGenAlg – A genetic algorithm hyper-heuristic for selection hyper-heuristics, which distributes the implementation of the genetic algorithm over a multicore architecture.
  • GenProg  – A genetic programming hyper-heuristic for generation hyper-heuristics.
  • DistrGenProg – A genetic programming hyper-heuristic for generation hyper-heuristics, which distributes the genetic programming algorithm over a multicore architecture.