Results 1 
6 of
6
Linkage Learning via Probabilistic Modeling in the ECGA
, 1999
"... The goal of linkage learning, or building block identification, is the creation of a more effective genetic algorithm (GA). This paper explores the relationship between the linkagelearning problem and that of learning probability distributions over multivariate spaces. Herein, it is argued that th ..."
Abstract

Cited by 193 (4 self)
 Add to MetaCart
The goal of linkage learning, or building block identification, is the creation of a more effective genetic algorithm (GA). This paper explores the relationship between the linkagelearning problem and that of learning probability distributions over multivariate spaces. Herein, it is argued that these problems are equivalent. Using a simple but effective approach to learning distributions, and by implication linkage, this paper reveals the existence of GAlike algorithms that are potentially orders of magnitude faster and more accurate than the simple GA. I. Introduction Linkage learning in genetic algorithms (GAs) is the identification of building blocks to be conserved under crossover. Theoretical studies have shown that if an effective linkagelearning GA were developed, it would hold significant advantages over the simple GA (2). Therefore, the task of developing such an algorithm has drawn significant attention. Past approaches to developing such an algorithm have focused on ev...
The Bivariate Marginal Distribution Algorithm
, 1999
"... The paper deals with the Bivariate Marginal Distribution Algorithm (BMDA). BMDA is an extension of the Univariate Marginal Distribution Algorithm (UMDA). It uses the pair gene dependencies in order to improve algorithms that use simple univariate marginal distributions. BMDA is a special case of the ..."
Abstract

Cited by 90 (19 self)
 Add to MetaCart
The paper deals with the Bivariate Marginal Distribution Algorithm (BMDA). BMDA is an extension of the Univariate Marginal Distribution Algorithm (UMDA). It uses the pair gene dependencies in order to improve algorithms that use simple univariate marginal distributions. BMDA is a special case of the Factorization Distribution Algorithm, but without any problem specic knowledge in the initial stage. The dependencies are being discovered during the optimization process itself. In this paper BMDA is described in detail. BMDA is compared to dierent algorithms including the simple genetic algorithm with dierent crossover methods and UMDA. For some tness functions the relation between problem size and the number of tness evaluations until convergence is shown. 1. Introduction Genetic algorithms work with populations of strings of xed length. In this paper binary strings will be considered. From current population better strings are selected at the expense of worse ones. New strings ar...
Evolutionary algorithm with the guided mutation for the maximum clique problem
 IEEE Transactions on Evolutionary Computation
, 2005
"... Abstract—Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutio ..."
Abstract

Cited by 31 (11 self)
 Add to MetaCart
Abstract—Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutions in the search space) is not directly used for generating offspring in most existing estimation of distribution algorithms. This paper introduces a new operator, called guided mutation. Guided mutation generates offspring through combination of global statistical information and the location information of solutions found so far. An evolutionary algorithm with guided mutation (EA/G) for the maximum clique problem is proposed in this paper. Besides guided mutation, EA/G adopts a strategy for searching different search areas in different search phases. Marchiori’s heuristic is applied to each new solution to produce a maximal clique in EA/G. Experimental results show that EA/G outperforms the heuristic genetic algorithm of Marchiori (the best evolutionary algorithm reported so far) and a MIMIC algorithm on DIMACS benchmark graphs. Index Terms—Estimation of distribution algorithms, evolutionary algorithm, guided mutation, heuristics, hybrid genetic algorithm, maximum clique problem (MCP). I.
Experimental Study: Hypergraph Partitioning Based on the Simple and Advanced Genetic Algorithm BMDA and
 In Proceedings of the Fifth International Conference on Soft Computing
, 1999
"... Abstract: This paper is an experimental study on hypegraph partitioning using the simple genetic algorithm (GA) based on the schema theorem and the advanced algorithms based on the estimation of distribution of promising solution. Primarily we have implemented a simple GA based on the GaLib library[ ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
Abstract: This paper is an experimental study on hypegraph partitioning using the simple genetic algorithm (GA) based on the schema theorem and the advanced algorithms based on the estimation of distribution of promising solution. Primarily we have implemented a simple GA based on the GaLib library[Gal94] and some hybrid variant included a fast heuristics to speed up the convergence of the optimization process. Secondly we have implemented the Univariate Marginal Distribution algorithm (UMDA) and the Bivariate Marginal Distribution algorithm (BMDA), both have been published even recently[Pel98] and used a share version of a superior new program BOA based on the Bayesian Optimization Algorithm [Pel99]. We have also extended the BMDA algorithm to a new version with finite alphabet encoding of chromozomes and new metric that enables the mway partitioning graphs. The aim of our paper is to test the efficiency of new approaches for discrete combinatorial problems represented by hypergraph partitioning. Key words: decomposition, hypergraph partitioning, simple and hybrid GA, estimation of distribution algorithm, Bayesian network. 1
Marginal Distributions in Evolutionary Algorithms
 In Proceedings of the International Conference on Genetic Algorithms Mendel ’98
, 1999
"... In this paper, the description of two gene pool recombination operators is described. Both operators are based on the estimation of the distribution of the parents and its use to generate new individuals. The Univariate Marginal Distribution Algorithm (UMDA) uses simple univariate distributions. ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
In this paper, the description of two gene pool recombination operators is described. Both operators are based on the estimation of the distribution of the parents and its use to generate new individuals. The Univariate Marginal Distribution Algorithm (UMDA) uses simple univariate distributions. It is a perfect algorithm for linear problems. The Bivariate Marginal Distribution Algorithm (BMDA) is an extension of UMDA. In BMDA, the most important pair dependencies are taken into account. The dependencies are measured by the Pearson's chisquare statistics. The structure of a problem is being discovered during an optimization. BMDA works well for linear problems as well as for problems with interacting genes. 1 Introduction Evolutionary algorithms work over populations of strings. The main schema of evolutionary algorithms is simple. The initial population is generated randomly. From the current population, a set of high quality individuals is selected first. The better the indi...
Population Optimization Algorithm Based on ICA
 In IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks
, 2000
"... this paper, we propose a new population optimization algorithm called Univariate Marginal Distribution Algorithm with Independent Component Analysis (UMDA/ICA). Our main idea is to incorporate ICA into UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
this paper, we propose a new population optimization algorithm called Univariate Marginal Distribution Algorithm with Independent Component Analysis (UMDA/ICA). Our main idea is to incorporate ICA into UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.