1042 Words5 Pages

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...

... middle of paper ...

...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.

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...

... middle of paper ...

...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.

Related

## Evolutionary Algorithm and Cloud Computing

970 Words | 4 PagesEvolutionary Algorithm and Discussions Cloud computing provides variety of internet on demand services such as software, infrastructure and data storage. for the purpose of provision of private service to the user, there is possibility to use multi-level password creation and documentation or authentication techniques. This technology assists in creating the password in several levels of the company. So, that the strict documentation and authorization is possible. The levels of security in cloud

## evolutionary algorithm

1060 Words | 5 Pagespapers like [28] are use univariate EDAs in continuous environments and [38] is another paper that uses EDAs in discrete environments. Besides, variant Particle Swarm Optimization (PSO) algorithms proposed on the DOPs provide good results. Therefore, to compare the results of MAMEDA we use [29] and [62]. PSO-CP algorithm [29] are utilized a new PSO model, called PSO with composite particles to address DOPs. In [62] is proposed a MA which hybridizes PSO with a fuzzy cognition local search technique on

## Application of Evolutionary Algorithm

1654 Words | 7 PagesIntroduction Evolutionary algorithm (EA) is defined as the set of purpose that used to solve any class of elements puzzle that related to the mathematical rules. Other than that, evolutionary algorithm is one of the problems solving to reduce or maximize a real function by analytically choosing the values of real or integer variables from interior of an allowed set. In artificial intelligence, an EA is a subgroup of evolutionary computation, a general population-based meta-experimental operation

## Evolutionary Computation Algorithm Essay

2072 Words | 9 Pages2 Evolutionary Computation Algorithms 2.1 Introduction Evolutionary computation algorithms are based on the biology evolution theory. Have you ever heard the phrase "Survival of the fittest" - Herbert Spencer? Imagine an island of castaways and the only resource of food are coconut trees. It make sense that whoever is tall enough will feed and survive. A few years after those people will match and give birth to children with better characteristics, in our case taller. So as the years gone by and

## Recent Trends in Document Clustering with Evolutionary-Based Algorithms

742 Words | 3 Pagesa number of statistical algorithms had been applied to perform clustering to the data including the text documents. There are recent endeavors to enhance the performance of the clustering with the optimization based algorithms such as the evolutionary algorithms. Thus, document clustering with evolutionary algorithms became an emerging topic that gained more attention in the recent years. This paper presents an up-to-date review fully devoted to evolutionary algorithms designed for document clustering

## Linear Quadratic Optimal Control System Design Using Evolutionary Algorithms

2475 Words | 10 PagesSelecting appropriate weighting matrices for desired Linear Quadratic Regulator (LQR) controller design using evolutionary algorithms is presented in this paper. Obviously, it is not easy to determine the appropriate weighting matrices for an optimal control system and a suitable systematic method is not presented for this goal. In other words, there isn’t direct relationship between weighting matrices and control system characteristics and selecting these matrices is done using by trial and error

## Automated_Software_Test_Data_Optimization_Using_AI (IS-II Short Report)Arshad_087104

3872 Words | 16 Pagesimplement and validate a technique of AI that uses genetic algorithm for the optimization of software test data. A genetic algorithm emerges from the evaluation of natural species in searching for the optimal solution to a problem. In this paper authors have used multi objective genetic algorithm for implementing the proposed algorithm. For this purpose, Authors have chosen NSGA (Non-Dominated Sorting Genetic Algorithm). It is a very effective algorithm and has rarely been used for the test data optimization

## Fuzzy Logic Control Systems

1063 Words | 5 PagesOne great barrier that has stood in front of computer programmers is that of finally realizing a dream of building a computer system that realistically models human thinking. The ethics of realizing such a dream are widely debated. Many believe it would be an extremely dangerous thing to accomplish, but that hasn’t stopped many from trying. The two main systems that have been developed so far that come closest to accomplishing this goal are neural networks and fuzzy logic control systems. This

## HEN

528 Words | 3 PagesIn present day, it is necessary to reduce energy consumption, especially when fossil fuel is decreasing and energy price is growing. Over the last few decades, heat exchanger network synthesis (HENS) and optimization topic is the most-studied topic in process integration because of its task in the energy recovery. There are two different methods for the synthesis and optimization of HEN problems, including pinch analysis and mathematical programming. Pinch technology is based on thermodynamic approaches

## Architecting Digital-to-Analog Converters Using Game-Theoretic Configurations

2699 Words | 11 Pagestheorists [6]. The drawback of this type of solution, however, is that the seminal real-time algorithm for the evaluation of Moore's Law by W. Brown et al. [6] runs in (logn) time. Contrarily, amphibious communication might not be the panacea that information theorists expected. Such a claim is largely an unproven purpose but fell in line with our expectations. Existing ubiquitous and signed algorithms use the development of the Ethernet to request the study of telephony [10]. It should be noted

### Evolutionary Algorithm and Cloud Computing

970 Words | 4 Pages### evolutionary algorithm

1060 Words | 5 Pages### Application of Evolutionary Algorithm

1654 Words | 7 Pages### Evolutionary Computation Algorithm Essay

2072 Words | 9 Pages### Recent Trends in Document Clustering with Evolutionary-Based Algorithms

742 Words | 3 Pages### Linear Quadratic Optimal Control System Design Using Evolutionary Algorithms

2475 Words | 10 Pages### Automated_Software_Test_Data_Optimization_Using_AI (IS-II Short Report)Arshad_087104

3872 Words | 16 Pages### Fuzzy Logic Control Systems

1063 Words | 5 Pages### HEN

528 Words | 3 Pages### Architecting Digital-to-Analog Converters Using Game-Theoretic Configurations

2699 Words | 11 Pages