scholarly journals On the Optimal Convergence Probability of Univariate Estimation of Distribution Algorithms

2011 ◽  
Vol 19 (2) ◽  
pp. 225-248 ◽  
Author(s):  
Reza Rastegar
Keyword(s):  
Genetic Algorithm ◽  
Confidence Level ◽  
Incremental Learning ◽  
Optimal Solution ◽  
Population Based ◽  
Sufficient Condition ◽  
Estimation Of Distribution Algorithms ◽  
Compact Genetic Algorithm ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the optimal solution with a predefined confidence level.

FAULT DIAGNOSIS OF MULTIPROCESSOR SYSTEMS BASED ON GENETIC AND ESTIMATION OF DISTRIBUTION ALGORITHMS: A PERFORMANCE EVALUATION

2010 ◽  
Vol 19 (01) ◽  
pp. 1-18 ◽  
Author(s):  
ELIAS P. DUARTE ◽  
AURORA T. R. POZO ◽  
BOGDAN T. NASSU
Keyword(s):  
Large Scale ◽  
Optimal Solution ◽  
Population Based ◽  
Experimental Results ◽  
System Level ◽  
Multiprocessor Systems ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Average Fitness

As faults are unavoidable in large scale multiprocessor systems, it is important to be able to determine which units of the system are working and which are faulty. System-level diagnosis is a long-standing realistic approach to detect faults in multiprocessor systems. Diagnosis is based on the results of tests executed on the system units. In this work we evaluate the performance of evolutionary algorithms applied to the diagnosis problem. Experimental results are presented for both the traditional genetic algorithm (GA) and specialized versions of the GA. We then propose and evaluate specialized versions of Estimation of Distribution Algorithms (EDA) for system-level diagnosis: the compact GA and Population-Based Incremental Learning both with and without negative examples. The evaluation was performed using four metrics: the average number of generations needed to find the solution, the average fitness after up to 500 generations, the percentage of tests that got to the optimal solution and the average time until the solution was found. An analysis of experimental results shows that more sophisticated algorithms converge faster to the optimal solution.

Estimation of Distribution Algorithms Applied to History Matching

SPE Journal ◽  
2013 ◽  
Vol 18 (03) ◽  
pp. 508-517 ◽  
Author(s):  
Asaad Abdollahzadeh ◽  
Alan Reynolds ◽  
Mike Christie ◽  
David Corne ◽  
Glyn Williams ◽  
...  
Keyword(s):  
History Matching ◽  
Population Based ◽  
Fast Convergence ◽  
Bayesian Optimization ◽  
Evolutionary Strategies ◽  
Estimation Of Distribution Algorithms ◽  
Research Activity ◽  
Matching Problems ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

Summary The topic of automatically history-matched reservoir models has seen much research activity in recent years. History matching is an example of an inverse problem, and there is significant active research on inverse problems in many other scientific and engineering areas. While many techniques from other fields, such as genetic algorithms, evolutionary strategies, differential evolution, particle swarm optimization, and the ensemble Kalman filter have been tried in the oil industry, more recent and effective ideas have yet to be tested. One of these relatively untested ideas is a class of algorithms known as estimation of distribution algorithms (EDAs). EDAs are population-based algorithms that use probability models to estimate the probability distribution of promising solutions, and then to generate new candidate solutions. EDAs have been shown to be very efficient in very complex high-dimensional problems. An example of a state-of-the-art EDA is the Bayesian optimization algorithm (BOA), which is a multivariate EDA employing Bayesian networks for modeling the relationships between good solutions. The use of a Bayesian network leads to relatively fast convergence as well as high diversity in the matched models. Given the relatively limited number of reservoir simulations used in history matching, EDA-BOA offers the promise of high-quality history matches with a fast convergence rate. In this paper, we introduce EDAs and describe BOA in detail. We show results of the EDA-BOA algorithm on two history-matching problems. First, we tune the algorithm, demonstrate convergence speed, and search diversity on the PUNQ-S3 synthetic case. Second, we apply the algorithm to a real North Sea turbidite field with multiple wells. In both examples, we show improvements in performance over traditional population-based algorithms.

Estimation of Distribution Algorithms for Feature Subset Selection in Large Dimensionality Domains

Data Mining ◽  
2011 ◽  
pp. 97-116 ◽  
Author(s):  
Inaki Inza ◽  
Pedro Larranaga ◽  
Basilio Sierra
Keyword(s):  
Probabilistic Models ◽  
Subset Selection ◽  
Population Based ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution Algorithms ◽  
Text Learning ◽  
Estimation Of Distribution ◽  
Selection Tasks ◽  
Distribution Algorithms

Feature Subset Selection (FSS) is a well-known task of Machine Learning, Data Mining, Pattern Recognition or Text Learning paradigms. Genetic Algorithms (GAs) are possibly the most commonly used algorithms for Feature Subset Selection tasks. Although the FSS literature contains many papers, few of them tackle the task of FSS in domains with more than 50 features. In this chapter we present a novel search heuristic paradigm, called Estimation of Distribution Algorithms (EDAs), as an alternative to GAs, to perform a population-based and randomized search in datasets of a large dimensionality. The EDA paradigm avoids the use of genetic crossover and mutation operators to evolve the populations. In absence of these operators, the evolution is guaranteed by the factorization of the probability distribution of the best solutions found in a generation of the search and the subsequent simulation of this distribution to obtain a new pool of solutions. In this chapter we present four different probabilistic models to perform this factorization. In a comparison with two types of GAs in natural and artificial datasets of a large dimensionality, EDAbased approaches obtain encouraging results with regard to accuracy, and a fewer number of evaluations were needed than used in genetic approaches.

Space Complexity of Estimation of Distribution Algorithms

2005 ◽  
Vol 13 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Yong Gao ◽  
Joseph Culberson
Keyword(s):  
Genetic Algorithm ◽  
Bayesian Network ◽  
Optimization Problem ◽  
Space Complexity ◽  
Estimation Of Distribution Algorithms ◽  
Additive Functions ◽  
Problem Size ◽  
Interaction Structure ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.

scholarly journals Minimization of the Total Traveling Distance and Maximum Distance by Using a Transformed-Based Encoding EDA to Solve the Multiple Traveling Salesmen Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
S. H. Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Maximum Distance ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Hard Problems ◽  
Distribution Algorithms

Estimation of distribution algorithms (EDAs) have been used to solve numerous hard problems. However, their use with in-group optimization problems has not been discussed extensively in the literature. A well-known in-group optimization problem is the multiple traveling salesmen problem (mTSP), which involves simultaneous assignment and sequencing procedures and are shown in different forms. This paper presents a new algorithm, namedEDAMLA, which is based on self-guided genetic algorithm with a minimum loading assignment (MLA) rule. This strategy uses the transformed-based encoding approach instead of direct encoding. The solution space of the proposed method is onlyn!. We compare the proposed algorithm against the optimal direct encoding technique, the two-part encoding genetic algorithm, and, in experiments on 34 TSP instances drawn from the TSPLIB, find that its solution space isn!n-1m-1. The scale of the experiments exceeded that presented in prior studies. The results show that the proposed algorithm was superior to the two-part encoding genetic algorithm in terms of minimizing the total traveling distance. Notably, the proposed algorithm did not cause a longer traveling distance when the number of salesmen was increased from 3 to 10. The results suggest that EDA researchers should employ the MLA rule instead of direct encoding in their proposed algorithms.

LZW Chromosome Encoding in Estimation of Distribution Algorithms

2013 ◽  
Vol 4 (4) ◽  
pp. 41-61
Author(s):  
Orawan Watchanupaporn ◽  
Worasait Suwannik
Keyword(s):  
Genetic Algorithm ◽  
Model Building ◽  
Fitness Landscape ◽  
Estimation Of Distribution Algorithm ◽  
Experimental Results ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Lzw Algorithm

Estimation of distribution algorithm (EDA) can solve more complicated problems than its predecessor (Genetic Algorithm). EDA uses various methods to probabilistically model a group of highly fit individuals. Calculating the model in sophisticated EDA is very time consuming. To reduce the model building time, the authors propose compressed chromosome encoding. A chromosome is encoded using a format that can be decompressed by the Lempel-Ziv-Welch (LZW) algorithm. The authors combined LZW encoding with various EDAs and termed the class of algorithms Lempel-Ziv-Welch Estimation of Distribution Algorithms (LZWEDA). Experimental results show that LZWEDA significantly outperforms the original EDA. Finally, the authors analyze how LZW encoding transforms a fitness landscape.

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