evolutionary algorithm

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In this section, we discuss the relation works with MAMEDA.
It composes of two subsections. In subsection A, the framework of EDA is described, and we have tried to present a category of the methods are used for estimation the structures in multivariate EDAs. In subsection B, we review some of the MAs, EAs and EADs method presented on the
DOPs.
A. EDA
The general steps of EDAs have been used in stationary optimization problems are as follows [35]:
1) Generate initial populations D with uniform distribution of variables, and evaluate them.
2) Select N promising individuals from the populationD.
3) Estimate best structure based on the selected individuals.
4) Sampling new individuals based on the parameter estimated. 5) Evaluate new individuals, and replace old individuals.
6) If a termination criterion is not met, go to step 2.
The above steps usually are used for multivariate EADs. In univariate EDA, there is not structure learning, and bivariate
EDAs are different in structure learning and sampling steps.
The structure learning is more important part and there are several works in this field. So, we focus on this step.
We assume each individual in search space has n dimension or n variables. In the structure learning we must calculate the following probability,  is parents set of variable  , if univariate EDAs is used then   ∅. As we described in introduction, the probability graphical models (PGM) are used for structure representation, or presentation of the estimated structure. For example, in Fig.
I is represented a structure with four variables that used of
Gaussian model.
Structure learning consists of two components. First, with attention to the selected individuals, the best structure is estimated, approximately. Se...

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...gorithm that proposed for dynamic environments. In that paper two different probabilities based primal dual mapping (PDM) operators are used. PDM considers in each generation a set of individuals are selected for recombination and mutation operators. Then some of the best chromosomes are selected to process in next generation.
A set of lowest fitness chromosomes are also selected to evaluate their duals. In [60], researchers are presented a genetic algorithm based memetic algorithm on DOPs. The proposed MA uses two types of hill climbing methods as local searches, that called greedy crossover based hill climbing
(GCHC), and steepest mutation based hill climbing (SMHC).
In [65], the author tries to store the best individual in each generation, and in the next generation, it is used as the base for creation of immigrant individuals, into the new population by
mutation.
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