Results 1  10
of
46
A Survey of Optimization by Building and Using Probabilistic Models
 COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
, 1999
"... This paper summarizes the research on populationbased probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of ge ..."
Abstract

Cited by 275 (82 self)
 Add to MetaCart
This paper summarizes the research on populationbased probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated. All methods are classified into a few classes according to the complexity of the class of models they use. Algorithms from each of these classes are briefly described and their strengths and weaknesses are discussed.
The CMA Evolution Strategy: A Comparing Review
 STUDFUZZ
, 2006
"... Derived from the concept of selfadaptation in evolution strategies, the CMA (Covariance Matrix Adaptation) adapts the covariance matrix of a multivariate normal search distribution. The CMA was originally designed to perform well with small populations. In this review, the argument starts out with ..."
Abstract

Cited by 45 (16 self)
 Add to MetaCart
Derived from the concept of selfadaptation in evolution strategies, the CMA (Covariance Matrix Adaptation) adapts the covariance matrix of a multivariate normal search distribution. The CMA was originally designed to perform well with small populations. In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm. Commonalities and differences to continuous Estimation of Distribution Algorithms are analyzed. The aspects of reliability of the estimation, overall step size control, and independence from the coordinate system (invariance) become particularly important in small populations sizes. Consequently, performing the adaptation task with small populations is more intricate.
Continuous Iterated Density Estimation Evolutionary Algorithms Within The IDEA Framework
, 2000
"... In this paper, we formalize the notion of performing optimization by iterated density estimation evolutionary algorithms as the IDEA framework. These algorithms build probabilistic models and estimate probability densities based upon a selection of available points. We show how these probabili ..."
Abstract

Cited by 44 (4 self)
 Add to MetaCart
In this paper, we formalize the notion of performing optimization by iterated density estimation evolutionary algorithms as the IDEA framework. These algorithms build probabilistic models and estimate probability densities based upon a selection of available points. We show how these probabilistic models can be built and used for dierent probability density functions within the IDEA framework. We put the emphasis on techniques for vectors of continuous random variables and thereby introduce new continuous evolutionary optimization algorithms.
Optimization in continuous domains by learning and simulation of Gaussian networks
"... This paper shows how the Gaussian network paradigm can be used to solve optimization problems in continuous domains. Some methods of structure learning from data and simulation of Gaussian networks are applied in the Estimation of Distribution Algorithm (EDA) as well as new methods based on in ..."
Abstract

Cited by 32 (4 self)
 Add to MetaCart
This paper shows how the Gaussian network paradigm can be used to solve optimization problems in continuous domains. Some methods of structure learning from data and simulation of Gaussian networks are applied in the Estimation of Distribution Algorithm (EDA) as well as new methods based on information theory are proposed. Experimental results are also presented. 1 Estimation of Distribution Algorithms approaches in continuous domains Figure 1 shows a schematic of the EDA approach for continuous domains. We will use x = (x 1 ; : : : ; xn ) to denote individuals, and D l to denote the population of N individuals in the lth generation. Similarly, D Se l will represent the population of the selected Se individuals from D l . In the EDA [9] our interest will be to estimate f(x j D Se ), that is, the joint probability density function over one individual x being among the selected individuals. We denote as f l (x) = f l (x j D Se l 1 ) the joint density of the lth genera...
Expanding From Discrete To Continuous Estimation Of Distribution Algorithms: The IDEA
 In Parallel Problem Solving From Nature  PPSN VI
, 2000
"... . The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, i ..."
Abstract

Cited by 30 (7 self)
 Add to MetaCart
. The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, is a combination of the recombination and mutation steps used in evolutionary algorithms. We introduce the framework named IDEA to formalize this notion. By combining continuous probability theory with techniques from existing algorithms, this framework allows us to dene new continuous evolutionary optimization algorithms. 1 Introduction Algorithms in evolutionary optimization guide their search through statistics based on a vector of samples, often called a population. By using this stochastic information, non{deterministic induction is performed in order to attempt to use the structure of the search space and thereby aid the search for the optimal solution. In order to perform induct...
Realvalued Evolutionary Optimization using a Flexible Probability Density Estimator
 Proceedings of the GECCO 1999 Genetic and Evolutionary Computation Conference
, 1999
"... PopulationBased Incremental Learning (PBIL) is an abstraction of a genetic algorithm, which solves optimization problems by explicitly constructing a probabilistic model of the promising regions of the search space. At each iteration the model is used to generate a population of candidate sol ..."
Abstract

Cited by 27 (1 self)
 Add to MetaCart
PopulationBased Incremental Learning (PBIL) is an abstraction of a genetic algorithm, which solves optimization problems by explicitly constructing a probabilistic model of the promising regions of the search space. At each iteration the model is used to generate a population of candidate solutions and is itself modified in response to these solutions. Through the extension of PBIL to Realvalued search spaces, a more powerful and general algorithmic framework arises which enables the use of arbitrary probability density estimation techniques in evolutionary optimization. To illustrate the usefulness of the framework, we propose and implement an evolutionary algorithm which uses a finite Adaptive Gaussian mixture model density estimator. This method offers considerable power and flexibility in the forms of the density which can be effectively modeled. We discuss the general applicability of the framework, and suggest that future work should lead to the developmen...
An Algorithmic Framework For Density Estimation Based Evolutionary Algorithms
, 1999
"... The direct application of statistics to stochastic optimization in evolutionary computation has become more important and present over the last few years. With the introduction of the notion of the Estimation of Distribution Algorithm (EDA), a new line of research has been named. The application are ..."
Abstract

Cited by 24 (5 self)
 Add to MetaCart
The direct application of statistics to stochastic optimization in evolutionary computation has become more important and present over the last few years. With the introduction of the notion of the Estimation of Distribution Algorithm (EDA), a new line of research has been named. The application area so far has mostly been the same as for the classic genetic algorithms, being the binary vector encoded problems. The most important aspect in the new algorithms is the part where probability densities are estimated. In probability theory, a distinction is made between discrete and continuous distributions and methods. Using the rationale for density estimation based evolutionary algorithms, we present an algorithmic framework for them, named IDEA. This allows us to define such algorithms for vectors of both continuous and discrete random variables, combining techniques from existing EDAs as well as density estimation theory. The emphasis is on techniques for vectors of continuous random variables, for which we present new algorithms in the field of density estimation based evolutionary algorithms, using two different density estimation models.
Parallel estimation of distribution algorithms
, 2002
"... The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion ..."
Abstract

Cited by 22 (3 self)
 Add to MetaCart
The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion of a new formal description of EDA algorithm. This high level concept can be used to compare the generality of various probabilistic models by comparing the properties of underlying mappings. Also, some convergence issues are discussed and theoretical ways for further improvements are proposed. 2. Development of new probabilistic model and methods capable of dealing with continuous parameters. The resulting Mixed Bayesian Optimization Algorithm (MBOA) uses a set of decision trees to express the probability model. Its main advantage against the mostly used IDEA and EGNA approach is its backward compatibility with discrete domains, so it is uniquely capable of learning linkage between mixed continuousdiscrete genes. MBOA handles the discretization of continuous parameters as an integral part of the learning process, which outperforms the histogrambased
Learning to Play PacMan: An Evolutionary, Rulebased Approach
 In Evolutionary Computation, 2003. CEC ’03. The 2003 Congress on Evolutionary Computation
, 2003
"... PacMan is a wellknown, realtime computer game that provides an interesting platform for research. This paper describes an initial approach to developing an artificial agent that replaces the human to play a simplified version of PacMan. The agent is specified as a simple finite state machine and ..."
Abstract

Cited by 15 (1 self)
 Add to MetaCart
PacMan is a wellknown, realtime computer game that provides an interesting platform for research. This paper describes an initial approach to developing an artificial agent that replaces the human to play a simplified version of PacMan. The agent is specified as a simple finite state machine and ruleset, with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing PacMan, rather than a preprogrammed mazesolving method. The agent adaptively "learns" through the application of populationbased incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.
Probabilistic ModelBuilding Genetic Algorithms in Permutation Representation Domain Using Edge Histogram
 Proc. of the 7th Int. Conf. on Parallel Problem Solving from Nature (PPSN VII
, 2002
"... Abstract. Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic modeling. In this scheme, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. ..."
Abstract

Cited by 14 (8 self)
 Add to MetaCart
Abstract. Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic modeling. In this scheme, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. In this paper, we have proposed probabilistic modelbuilding genetic algorithms (PMBGAs) in permutation representation domain using edge histogram based sampling algorithms (EHBSAs). Two types of sampling algorithms, without template (EHBSA/WO) and with template (EHBSA/WT), are presented. The results were tested in the TSP and showed EHBSA/WT worked fairly well with a small population size in the test problems used. It also worked better than wellknown traditional twoparent recombination operators. 1