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.
Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage le...
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ISBN:
(纸本)9781424481262
Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage learning problem. Numerous attempts have been carried out to solve this problem with various degree of success. In this paper, we employ an effective algorithm to cluster a dependency structure matrix (DSM) which can correctly identify the linkage groups. Once all the linkage groups are identified, a simple genetic algorithm using BB-wise crossover can easily solve hard optimization problems. Experimental results with a number of deceptive functions with various sizes presented to show the efficiency enhancement obtained by the proposed method. The results are also compared with bayesian optimization algorithm, a well-known evolutionary optimizer, to demonstrate this improvement.
The stochastic schemata exploiter (SSE), which is one of the evolutionary algorithms based on schemata theory, was presented by Aizawa. The convergence speed of SSE is much faster than simple genetic algorithm. It sac...
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The stochastic schemata exploiter (SSE), which is one of the evolutionary algorithms based on schemata theory, was presented by Aizawa. The convergence speed of SSE is much faster than simple genetic algorithm. It sacrifices somewhat the global search performance. This paper describes an improved algorithm of SSE,which is named as cross-generational elitist selection SSE (cSSE). In cSSE, the use of the cross-generational elitist selection enhances the diversity of the individuals in the population and therefore, the global search performance is improved. In the numerical examples, cSSE is compared with genetic algorithm with minimum generation gap (MGG),bayesian optimization algorithm (BOA), and SSE. The results show that cSSE has fast convergence and good global search performance.
A kind of cooperative dynamic path planning algorithm for multiple UCAVs in dynamic environments is presented. If there is a pop-up threat when UCAVs are enroute to terminal point, the algorithm calculates the value o...
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ISBN:
(纸本)9781424427994
A kind of cooperative dynamic path planning algorithm for multiple UCAVs in dynamic environments is presented. If there is a pop-up threat when UCAVs are enroute to terminal point, the algorithm calculates the value of the threat. If the pop-up threat was a more valuable target, the algorithm assigns the target to one of UCAVs. The UCAV attack the target in time. If the pop-up threat was not a more valuable target, the algorithm plans path for all UCAVs to evade the threat. Due to time available to plan and the feasibility of the planning result are considered, the algorithm is able to process the new information at any time. The simulation results demonstrate the method is able to make UCAV to complete its task more autonomously.
A bayesian optimization algorithm (BOA) for unmanned aerial vehicle (UAV) path planning is presented, which involves choosing path representation and designing appropriate metric to measure the quality of the construc...
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ISBN:
(纸本)038723151X
A bayesian optimization algorithm (BOA) for unmanned aerial vehicle (UAV) path planning is presented, which involves choosing path representation and designing appropriate metric to measure the quality of the constructed network. Unlike our previous work in which genetic algorithm (GA) was used to implement implicit teaming, the teaming in the proposed algorithm is explicit, and the BOA is applied to implement such explicit teaming by building a bayesian network of the joint distribution of solutions. Experimental results demonstrate that this approach can overcome some drawbacks of other path planning algorithms. It is also suggested that the teaming mechanism in the proposed approach might be suitable for other multivariate encoding problems.
Standoff land missile has started the road of far distance, beyond low altitude and high accuracy. Every step of the planning will affect the following steps in the path planning. The existing methods
Standoff land missile has started the road of far distance, beyond low altitude and high accuracy. Every step of the planning will affect the following steps in the path planning. The existing methods
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