# Main Loop of an EDA Main loop of an EDA. UTF-8 ```r edaRun(eda, f, lower, upper) ``` ## Arguments - `eda`: `EDA` instance. - `f`: Objective function. - `lower`: Lower bounds of the variables of the objective function. - `upper`: Upper bounds of the variables of the objective function. ## Details EDAs are implemented using S4 classes with generic functions for its main parts: seeding (`edaSeed`), selection (`edaSelect`), learning (`edaLearn`), sampling (`edaSample`), replacement (`edaReplace`), local optimization (`edaOptimize`), termination (`edaTerminate`), and reporting (`edaReport`). The following pseudocode illustrates the interactions between all the generic functions. It is a simplified version of the implementation of the `edaRun` function. ``` gen <- 0 fEvals <- 0 terminate <- FALSE while (!terminate) { gen <- gen + 1 if (gen == 1) { model <- NULL pop <- edaSeed(lower, upper) # Set popEval to the evaluation of each solution in pop. # Update fEvals. r <- edaOptimize(gen, pop, popEval, f, lower, upper) pop <- r$pop; popEval <- r$popEval } else { s <- edaSelect(gen, pop, popEval) selectedPop <- pop[s, ]; selectedEval <- popEval[s] model <- edaLearn(gen, model, selectedPop, selectedEval, lower, upper) sampledPop <- edaSample(gen, model, lower, upper) # Set sampledEval to the evaluation of each solution # in sampledPop. Update fEvals. r <- edaOptimize(gen, sampledPop, sampledEval, f, lower, upper) sampledPop <- r$pop; sampledEval <- r$popEval r <- edaReplace(gen, pop, popEval, sampledPop, sampledEval) pop <- r$pop; popEval <- r$popEval } edaReport(gen, fEvals, model, pop, popEval) terminate <- edaTerminate(gen, fEvals, pop, popEval) } ``` ## Returns An `EDAResult` instance. ## References Gonzalez-Fernandez Y, Soto M (2014). `copulaedas`: An Package for Estimation of Distribution Algorithms Based on Copulas. **Journal of Statistical Software**, 58 (9), 1-34. [http://www.jstatsoft.org/v58/i09/](http://www.jstatsoft.org/v58/i09/). ## See Also `EDA`, `EDAResult`, `edaIndepRuns`. ## Examples ```r setMethod("edaReport", "EDA", edaReportSimple) setMethod("edaTerminate", "EDA", edaTerminateCombined(edaTerminateMaxGen, edaTerminateEval)) DVEDA <- VEDA(vine = "DVine", copulas = c("normal"), indepTestSigLevel = 0.01, margin = "norm", popSize = 200, maxGens = 50, fEval = 0, fEvalTol = 1e-03) DVEDA@name <- "D-vine Estimation of Distribution Algorithm" result <- edaRun(DVEDA, fSphere, rep(-600, 5), rep(600, 5)) show(result) ```

edaRun function