scholarly journals Level-Based Analysis of Genetic Algorithms and Other Search Processes

2016 ◽  
Author(s):  
Dogan Corus ◽  
Duc-Cuong Dang ◽  
Anton V. Eremeev ◽  
Per Kristian Lehre
Keyword(s):  
Genetic Algorithms ◽  
Time Complexity ◽  
Fundamental Problem ◽  
Population Based ◽  
Rigorous Results ◽  
Expected Time ◽  
Sorting Problem ◽  
Estimation Of Distribution ◽  
A New Technique ◽  
Distribution Algorithms

AbstractUnderstanding how the time-complexity of evolutionary algorithms (EAs) depend on their parameter settings and characteristics of fitness landscapes is a fundamental problem in evolutionary computation. Most rigorous results were derived using a handful of key analytic techniques, including drift analysis. However, since few of these techniques apply effortlessly to population-based EAs, most time-complexity results concern simplified EAs, such as the (1 + 1) EA.This paper describes the level-based theorem, a new technique tailored to population-based processes. It applies to any non-elitist process where o spring are sampled independently from a distribution depending only on the current population. Given conditions on this distribution, our technique provides upper bounds on the expected time until the process reaches a target state.We demonstrate the technique on several pseudo-Boolean functions, the sorting problem, and approximation of optimal solutions in combina-torial optimisation. The conditions of the theorem are often straightfor-ward to verify, even for Genetic Algorithms and Estimation of Distribution Algorithms which were considered highly non-trivial to analyse. Finally, we prove that the theorem is nearly optimal for the processes considered. Given the information the theorem requires about the process, a much tighter bound cannot be proved.

Phylogenetic Differential Evolution

2014 ◽  
pp. 22-40 ◽  
Author(s):  
Vinícius Veloso de Melo ◽  
Danilo Vasconcellos Vargas ◽  
Marcio Kassouf Crocomo
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Building Blocks ◽  
Large Scale Systems ◽  
Estimation Of Distribution ◽  
Evolution Algorithm ◽  
A New Technique ◽  
Distribution Algorithms ◽  
Binary Problems

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

Linkage Identification by Fitness Difference Clustering

2006 ◽  
Vol 14 (4) ◽  
pp. 383-409 ◽  
Author(s):  
Miwako Tsuji ◽  
Masaharu Munetomo ◽  
Kiyoshi Akama
Keyword(s):  
Genetic Algorithms ◽  
Perturbation Methods ◽  
Computational Cost ◽  
Building Blocks ◽  
Estimation Of Distribution Algorithms ◽  
Novel Approach ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Class Of Functions ◽  
Dependency Detection

Genetic Algorithms perform crossovers effectively when linkage sets — sets of variables tightly linked to form building blocks — are identified. Several methods have been proposed to detect the linkage sets. Perturbation methods (PMs) investigate fitness differences by perturbations of gene values and Estimation of distribution algorithms (EDAs) estimate the distribution of promising strings. In this paper, we propose a novel approach combining both of them, which detects dependencies of variables by estimating the distribution of strings clustered according to fitness differences. The proposed algorithm, called the Dependency Detection for Distribution Derived from fitness Differences (D5), can detect dependencies of a class of functions that are difficult for EDAs, and requires less computational cost than PMs.

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.

Phylogenetic Differential Evolution

2011 ◽  
Vol 2 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Vinícius Veloso de Melo ◽  
Danilo Vasconcellos Vargas ◽  
Marcio Kassouf Crocomo
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Building Blocks ◽  
Large Scale Systems ◽  
Estimation Of Distribution ◽  
Evolution Algorithm ◽  
A New Technique ◽  
Distribution Algorithms ◽  
Binary Problems

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

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.

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