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Users of mobile apps sometimes express discomfort or concerns with what they see as unnecessary or intrusive permission requests by certain apps. However encouraging mobile app developers to request fewer permissions is challenging because there are ...
Probabilistic model-building genetic algorithms (PMBGAs), also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs), guide the search for the optimum by building and sampling explicit probabilistic ...
This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state ...
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies ...
Learning a good model structure is important to the efficient solving of problems by estimation of distribution algorithms. In this paper we present the results of a series of experiments, applying a structure learning algorithm for undirected ...
Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...
Epistasis correlation is a measure that estimates the strength of interactions between problem variables. This paper presents an empirical study of epistasis correlation on a large number of random problem instances of NK landscapes with nearest ...
The linkage tree genetic algorithm (LTGA) identifies linkages between problem variables using an agglomerative hierarchical clustering algorithm and linkage trees. This enables LTGA to solve many decomposable problems that are difficult with more ...
While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One of ...
Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...
Practitioners often have some information about the problem being solved, which may be represented as a graph of dependencies or correlations between problem variables. Similar information can also be obtained automatically, for example by mining the ...
This paper presents an experimental study of NK landscapes with the main focus on the relationship between (1) problem parameters, (2) various measures of problem difficulty of fitness landscapes, and (3) performance of hybrid evolutionary algorithms. ...
Numerous studies have shown that advanced estimation of distribution algorithms (EDAs) often discover spurious (unnecessary) dependencies. Nonetheless, only little prior work exists that would study the effects of spurious dependencies on EDA ...
Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...
This paper presents a class of NK landscapes with nearest-neighbor interactions and tunable overlap. The considered class of NK landscapes is solvable in polynomial time using dynamic programming; this allows us to generate a large number of random ...
This paper provides an in-depth empirical analysis of several hybrid evolutionary algorithms on the one-dimensional spin glass model with power-law interactions. The considered spin glass model provides a mechanism for tuning the effective range of ...
Hybridization of global and local search algorithms is a well-established technique for enhancing the efficiency of search algorithms. Hybridizing estimation of distribution algorithms (EDAs) has been repeatedly shown to produce better performance than ...
This paper analyzes the effects of an initial-population bias on the performance of the univariate marginal distribution algorithm (UMDA). The analysis considers two test problems: (1) onemax and (2) noisy onemax. Theoretical models are provided and ...
One of the primary advantages of estimation of distribution algorithms (EDAs) over many other stochastic optimization techniques is that they supply us with a roadmap of how they solve a problem. This roadmap consists of a sequence of probabilistic ...
This study analyzes performance of several genetic and evolutionary algorithms on randomly generated NK fitness landscapes with various values of n and k. A large number of NK problem instances are first generated for each n and k, and the global ...