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.

An Novel Estimation of Distribution Algorithm for TSP

2013 ◽  
Vol 373-375 ◽  
pp. 1089-1092
Author(s):  
Fa Hong Yu ◽  
Wei Zhi Liao ◽  
Mei Jia Chen
Keyword(s):  
Optimization Problem ◽  
Population Diversity ◽  
Combinatorial Optimization Problem ◽  
Estimation Of Distribution Algorithm ◽  
Traveling Salesman ◽  
Estimation Of Distribution Algorithms ◽  
Np Hard ◽  
Estimation Of Distribution ◽  
Np Hard Problem ◽  
Distribution Algorithms

Estimation of distribution algorithms (EDAs) is a method for solving NP-hard problem. But it is hard to find global optimization quickly for some problems, especially for traveling salesman problem (TSP) that is a classical NP-hard combinatorial optimization problem. To solve TSP effectively, a novel estimation of distribution algorithm (NEDA ) is provided, which can solve the conflict between population diversity and algorithm convergence. The experimental results show that the performance of NEDA is effective.

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.

Sensibility of Linkage Information and Effectiveness of Estimated Distributions

2010 ◽  
Vol 18 (4) ◽  
pp. 547-579 ◽  
Author(s):  
Chung-Yao Chuang ◽  
Ying-ping Chen
Keyword(s):  
Probabilistic Models ◽  
Model Building ◽  
Estimation Of Distribution Algorithms ◽  
Problem Structure ◽  
Linkage Information ◽  
Estimation Of Distribution ◽  
Separable Problems ◽  
Distribution Algorithms ◽  
The Given ◽  
Possible Cause

The probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, it may not be possible to completely and accurately identify the whole problem structure by probabilistic modeling due to certain inherent properties of the given problem. In this work, we illustrate one possible cause of such situations with problems consisting of structures with unequal fitness contributions. Based on the illustrative example, we introduce a notion that the estimated probabilistic models should be inspected to reveal the effective search directions and further propose a general approach which utilizes a reserved set of solutions to examine the built model for likely inaccurate fragments. Furthermore, the proposed approach is implemented on the extended compact genetic algorithm (ECGA) and experiments are performed on several sets of additively separable problems with different scaling setups. The results indicate that the proposed method can significantly assist ECGA to handle problems comprising structures of disparate fitness contributions and therefore may potentially help EDAs in general to overcome those situations in which the entire problem structure cannot be recognized properly due to the temporal delay of emergence of some promising partial solutions.

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.

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.

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

2005 ◽  
Vol 13 (1) ◽  
pp. 43-66 ◽  
Author(s):  
J. M. Peña ◽  
J. A. Lozano ◽  
P. Larrañaga
Keyword(s):  
Bayesian Networks ◽  
Unsupervised Learning ◽  
Genetic Drift ◽  
Optimization Problems ◽  
Estimation Of Distribution Algorithm ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Global Optima ◽  
Effectiveness And Efficiency ◽  
Distribution Algorithms

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

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.

scholarly journals Fast Parabola Detection Using Estimation of Distribution Algorithms

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jose de Jesus Guerrero-Turrubiates ◽  
Ivan Cruz-Aceves ◽  
Sergio Ledesma ◽  
Juan Manuel Sierra-Hernandez ◽  
Jonas Velasco ◽  
...  
Keyword(s):  
Fitness Function ◽  
Input Image ◽  
Experimental Results ◽  
Computational Time ◽  
Estimation Of Distribution Algorithms ◽  
Random Boundary ◽  
Generalized Hough Transform ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Retinal Fundus

This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about93.61%on synthetic images and89%on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

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