This paper presents the real-coded extended compact genetic algorithms (rECGA) for decomposable real-valued optimization problems. Mutual information among real-valued variables is employed to measure variables interaction or dependency, and the variables clustering and aggregation algorithms are proposed to identify the substructures of a problem through partitioning variables. Then, mixture Gaussian probability density function is estimated to model the promising individuals for each substructure, and the sampling of multivariate Gaussian probability density function is done by adopting Cholesky decomposition. Finally, experiments on decomposable test functions are conducted. The results show that the rECGA is able to correctly identify the substructure of decomposable problems with linear or nonlinear correlations, and achieves a good scalability. ]]>

The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. This paper studies the influence of different selection and replacement methods on the accuracy of linkage learning in BOA. Results on concatenated m-k deceptive trap functions show that the model accuracy depends on a large extent on the choice of selection method and to a lesser extent on the replacement strategy used. Specifically, it is shown that linkage learning in BOA is more accurate with truncation selection than with tournament selection. The choice of replacement strategy is important when tournament selection is used, but it is not relevant when using truncation selection. On the other hand, if performance is our main concern, tournament selection and restricted tournament replacement should be preferred. These results aim to provide practitioners with useful information about the best way to tune BOA with respect to structural model accuracy and overall performance. ]]>

This paper presents a simple real-coded estimation of distribution algorithm (EDA) design using ?-ary extended compact genetic algorithm (?ECGA) and discretization methods. Specifically, the real-valued decision variables are mapped to discrete symbols of user-specified cardinality using discretization methods. The ?ECGA is then used to build the probabilistic model and to sample a new population based on the probabilistic model. The effect of alphabet cardinality and the selection pressure on the scalability of the real-coded ECGA (rECGA) method is investigated. The results show that the population size required by rECGA—to successfully solve a class of additivelyseparable problems—scales sub-quadratically with problem size and the number of function evaluations scales sub-cubically with problem size. The proposed rECGA is simple, making it amenable for further empirical and theoretical analysis. Moreover, the probabilistic models built in the proposed realcoded ECGA are readily interpretable and can be easily visualized. The proposed algorithm and the results presented in this paper are first step towards conducting a systematic analysis of real-coded EDAs and towards developing a design theory for development of scalable and robust real-coded EDAs. ]]>

The push for rapid innovation and creativity in this Internet age places a premium on eective integration of both human and computer-generated knowledge. One of the key components of a distributed and scalable environment for accomplishing this integration called DISCUS is the human-based genetic algorithm (HBGA)–a GA where humans perform genetic operations. This paper takes the first step towards designing a competent HBGA, which can enable humans to innovate quickly, reliably, and accurately. Specifically, this paper proposes a methodology for discovering building blocks from text documents including reports, chat, transcripts and e-mail. The proposed method has been applied to simple test problems and to a news article set. The results show that the proposed BB-identification methodology is eective and enables humans to eectively exchange the BBs for rapid innovation. ]]>

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