bayesian optimization algorithm (BOA) has been used with different local structures to represent more complex models and a variety of scoring metrics to evaluate bayesian network. But the combinatorial effects of thes...
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ISBN:
(纸本)9781605583266
bayesian optimization algorithm (BOA) has been used with different local structures to represent more complex models and a variety of scoring metrics to evaluate bayesian network. But the combinatorial effects of these elements on the performance of BOA have not been investigated yet. In this paper the performance of BOA is studied using two criteria: Number of fitness evaluations and structural accuracy of the model. It is shown that simple exact local structures like CPT in conjunction with complexity penalizing BIC metric outperforms others in terms of model accuracy. But considering number of fitness evaluations (efficiency) of the algorithm, CPT with other complexity penalizing metric K2P performs better.
Estimation of distribution algorithms (EDAs). since they were introduced, have been successfully used to solve discrete optimization problems and hence proven to be an effective methodology for discrete optimization. ...
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Estimation of distribution algorithms (EDAs). since they were introduced, have been successfully used to solve discrete optimization problems and hence proven to be an effective methodology for discrete optimization. To enhance the applicability of EDAs, researchers started to integrate EDAs with discretization methods such that the EDAs designed for discrete variables can be made capable of solving continuous optimization problems. In order to further our understandings of the collaboration between EDAs and discretization methods, in this paper, we propose a quality measure of discretization methods for EDAs. We then utilize the proposed quality measure to analyze three discretization methods: fixed-width histogram (FWH). fixed-height histogram (FHH), and greedy random split (GRS). Analytical measurements are obtained for FHH and FWH, and sampling measurements are conducted for FHH. FWH, and GRS. Furthermore, we integrate bayesian optimization algorithm (BOA), a representative EDA, with the three discretization methods to conduct experiments and to observe the performance difference. A good agreement is reached between the discretization quality measurements and the numerical optimization results. The empirical results show that the proposed quality measure can be considered as an indicator of the suitability for a discretization method to work with EDAs.
Estimation of distribution algorithms (EDAs), as an extension of genetic algorithms, samples new solutions from the probabilistic model, which characterizes the distribution of promising solutions in the search space ...
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Estimation of distribution algorithms (EDAs), as an extension of genetic algorithms, samples new solutions from the probabilistic model, which characterizes the distribution of promising solutions in the search space at each generation. This paper introduces and evaluates a novel estimation of a distribution algorithm, called L1-regularized bayesian optimization algorithm, L1BOA. In L1BOA, bayesian networks as probabilistic models are learned in two steps. First, candidate parents of each variable in bayesian networks are detected by means of L1-regularized logistic regression, with the aim of leading a sparse but nearly optimized network structure. Second, the greedy search, which is restricted to the candidate parent-child pairs, is deployed to identify the final structure. Compared with the bayesian optimization algorithm (BOA), L1BOA improves the efficiency of structure learning due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems show that L1BOA not only outperforms BOA when no prior knowledge about problem structure is available, but also achieves and even exceeds the best performance of BOA that applies explicit controls on network complexity. Furthermore, bayesian networks built by L1BOA and BOA during evolution are analysed and compared, which demonstrates that L1BOA is able to build simpler, yet more accurate probabilistic models.
Fault identification method provides a great enhancement by using evolutionary algorithms in complex mechatronics systems. A Mutation-based bayesian optimization algorithm is presented to improve the efficiency of Bay...
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ISBN:
(纸本)9781424424948
Fault identification method provides a great enhancement by using evolutionary algorithms in complex mechatronics systems. A Mutation-based bayesian optimization algorithm is presented to improve the efficiency of bayesian optimization algorithm (BOA). The mutation operator which makes full use of local information is combined into BOA by diversity function. The original objective is to combine the global information and local information in order to avoid local optimum. According to the fault analysis of aircraft actuation systems, the program of BOA for fault identification is introduced. The scheme is illustrated through simulations applying the flight control system of a fighter. The simulation result show fault identification is achieved.
This paper proposes the incremental bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the bayesian network. iBOA is shown to b...
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ISBN:
(纸本)9781605581309
This paper proposes the incremental bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the bayesian network. iBOA is shown to be able to learn and exploit unrestricted bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
This paper introduces the notion of an entropy measurement for populations of candidate solutions in evolutionary algorithms, developing both conditional and joint entropy-based algorithms. We describe the inherent ch...
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This paper introduces the notion of an entropy measurement for populations of candidate solutions in evolutionary algorithms, developing both conditional and joint entropy-based algorithms. We describe the inherent characteristics of the entropy measurement and how these affect the search process. Following these discussions, we develop a recognition mechanism through which promising candidate solutions can be identified without the need of invoking costly evaluation functions. This on-demand evaluation strategy (ODES) is able to perform decision making tasks regardless of whether the actual fitness evaluation is necessary or not, making it an ideal efficiency enhancement technique for accelerating the computational process of evolutionary algorithms. Two different evolutionary algorithms, a traditional genetic algorithm and a multivariate estimation of distribution algorithm, are employed as example targets for the application of our on-demand evaluation strategy. Ultimately, experimental results confirm that our method is able to broadly improve the performance of various population-based global searchers. (C) 2011 Elsevier Inc. All rights reserved.
作者：
Yang, JiadongXu, HuaJia, PeifaTsinghua Univ
State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Dept Comp Sci & Technol Beijing 100084 Peoples R China
Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number of rules, combine supervised learning with genetic algorithms (GAS) to evolve rule-...
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Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number of rules, combine supervised learning with genetic algorithms (GAS) to evolve rule-based classification models. It has been shown that standard crossover operators in GAs do not guarantee an effective evolutionary search in many sophisticated problems that contain strong interactions between features. In this paper, we propose a Pittsburgh-style learning classifier system based on the bayesian optimization algorithm with the aim of improving the effectiveness and efficiency of the rule structure exploration. In the proposed method, classifiers are generated and recombined at two levels. At the lower level, single rules contained in classifiers are produced by sampling bayesian networks which characterize the global statistical information extracted from the current promising rules in the search space. At the higher level, classifiers are recombined by rule-wise uniform crossover operators to keep the semantics of rules in each classifier. Experimental studies on both artificial and real world binary classification problems show that the proposed method converges faster while achieving solutions with the same or even higher accuracy compared with the original Pittsburgh-style LCSs. (C) 2012 Elsevier Inc. All rights reserved.
Although BOA is effective at finding solutions for optimization problems, small population sizes in a model can result in premature convergence to a sub-optimal solution. One way of avoiding premature convergence is t...
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ISBN:
(纸本)9781595936974
Although BOA is effective at finding solutions for optimization problems, small population sizes in a model can result in premature convergence to a sub-optimal solution. One way of avoiding premature convergence is to increase population diversity with a mutation operator. In our experiments, we compare several mutation operators for use with BOA. We examine in detail the probabilistic model utilizing (PMU) bit flipping mutation operator. We compare the effectiveness of the PMU operator with standard BOA, self-adaptive evolution and local search of substructural neighborhoods.
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can ...
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Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.
One of the applications of DNA microarrays is recognizing the presence or absence of different biological components (targets) in a sample. Hence, the quality of the microarrays design which includes selecting short O...
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One of the applications of DNA microarrays is recognizing the presence or absence of different biological components (targets) in a sample. Hence, the quality of the microarrays design which includes selecting short Oligonucleotide sequences (probes) to be affixed on the surface of the microarray becomes a major issue. A good design is the one that contains the minimum possible number of probes while having an acceptable ability in identifying the targets existing in the sample. This paper focuses on the problem of computing the minimal set of probes which is able to identify each target of a sample, referred to as non-unique oligonucleotide probe selection. We present the application of an estimation of distribution algorithm (EDA) named bayesian optimization algorithm (BOA) to this problem, for the first time. The proposed approach considers integration of BOA and one simple heuristic introduced for the non-unique probe selection problem. The results provided by this approach compare favorably with the state-of-the-art methods in the single target case. While most of the recent research works on this problem has been focusing on the single target case only, we present the application of our method in integration with decoding approach in a multiobjective optimization framework for solving the problem in the case of multiple targets. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.
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