to guide an "optimal mixing" procedure which resembles a recombination operator but only accepts improving moves. We denote it as , where the notation indicates the transfer of the genetic material indexed by from to . Input: A family of subsets and a population Output: A population . for each in do for each in do choose a random := := if then return The LTGA does not implement typical selection operators, instead, selection is performed during recombination. Similar ideas have been usually applied into local-search heuristics and, in this sense, the LTGA can be seen as an hybrid method. In summary, one step of the LTGA is defined as Other Probability collectives (PC) Hill climbing with learning (HCwL) Estimation of multivariate normal algorithm (EMNA) Estimation of Bayesian networks algorithm (EBNA) Stochastic hill climbing with learning by vectors of normal distributions (SHCLVND) Real-coded PBIL Selfish Gene Algorithm (SG) Compact Differential Evolution (cDE) and its variants Compact Particle Swarm Optimization (cPSO) Compact Bacterial Foraging Optimization (cBFO) Probabilistic incremental program evolution (PIPE) Estimation of Gaussian networks algorithm (EGNA) Estimation multivariate normal algorithm with thresheld convergence Dependency Structure Matrix Genetic Algorithm (DSMGA) Related CMA-ES Cross-entropy method References Evolutionary computation Stochastic optimization