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1. 发明申请

US20050256684A1 System and method for estimation of a distribution algorithm 失效
标题翻译：用于估计分布算法的系统和方法
公开(公告)号：US20050256684A1
公开(公告)日：2005-11-17
申请号：US11033767
申请日：2005-01-11
申请人： Yaochu Jin , Bernhard Sendhoff , Tatsuya Okabe , Markus Olhofer
发明人： Yaochu Jin , Bernhard Sendhoff , Tatsuya Okabe , Markus Olhofer
IPC分类号： G06N3/00 , G06F17/10 , G06F17/18 , G06N3/12
CPC分类号： G06N3/126 , G06F2217/08
摘要： The underlying invention generally relates to the field of Estimation of Distribution Algorithm, especially to optimization problems, including single-objective optimization and Multi-Objective Optimization. The proposed method for optimization comprises six steps. In a first step it provides an initial population or a data set with a plurality of members respectively represented by parameter sets. Then one or a plurality of fitness functions are applied to evaluate the quality of the members of the population. In a third step offspring of the population is generated by means of a stochastic model using information from all members of the population. One or a plurality of fitness functions are applied to evaluate the quality of the offspring with respect to the underlying problem of the optimization. In a fifth step offspring is selected. Lastly the method goes back to the third step until the quality reaches a threshold value.
摘要翻译：基本发明一般涉及分布算法估计领域，尤其涉及优化问题，包括单目标优化和多目标优化。所提出的优化方法包括六个步骤。在第一步中，它提供初始种群或具有分别由参数集表示的多个成员的数据集。然后应用一个或多个健身功能来评估人群的成员的质量。在第三步中，人口的后代是通过使用来自所有成员的信息的随机模型产生的。应用一个或多个适应度函数来评估后代关于优化的基本问题的质量。在第五步中选择了后代。最后，该方法返回到第三步，直到质量达到阈值。

2. 发明授权

US07428514B2 System and method for estimation of a distribution algorithm 失效
标题翻译：用于估计分布算法的系统和方法
公开(公告)号：US07428514B2
公开(公告)日：2008-09-23
申请号：US11033767
申请日：2005-01-11
申请人： Yaochu Jin , Bernhard Sendhoff , Tatsuya Okabe , Markus Olhofer
发明人： Yaochu Jin , Bernhard Sendhoff , Tatsuya Okabe , Markus Olhofer
IPC分类号： G06N5/00
CPC分类号： G06N3/126 , G06F2217/08
摘要： The underlying invention generally relates to the field of Estimation of Distribution Algorithm, especially to optimization problems, including single-objective optimization and Multi-Objective Optimization.The proposed method for optimization comprises six steps. In a first step it provides an initial population or a data set with a plurality of members respectively represented by parameter sets. Then one or a plurality of fitness functions are applied to evaluate the quality of the members of the population. In a third step offspring of the population is generated by means of a stochastic model using information from all members of the population. One or a plurality of fitness functions are applied to evaluate the quality of the offspring with respect to the underlying problem of the optimization. In a fifth step offspring is selected. Lastly the method goes back to the third step until the quality reaches a threshold value.
摘要翻译：基本发明一般涉及分布算法估计领域，尤其涉及优化问题，包括单目标优化和多目标优化。所提出的优化方法包括六个步骤。在第一步中，它提供初始种群或具有分别由参数集表示的多个成员的数据集。然后应用一个或多个健身功能来评估人群的成员的质量。在第三步中，人口的后代是通过使用来自所有成员的信息的随机模型产生的。应用一个或多个适应度函数来评估后代关于优化的基本问题的质量。在第五步中选择了后代。最后，该方法返回到第三步，直到质量达到阈值。

3. 发明授权

US07363281B2 Reduction of fitness evaluations using clustering techniques and neural network ensembles 失效
标题翻译：使用聚类技术和神经网络集合减少健身评估
公开(公告)号：US07363281B2
公开(公告)日：2008-04-22
申请号：US11042991
申请日：2005-01-24
申请人： Yaochu Jin , Bernhard Sendhoff
发明人： Yaochu Jin , Bernhard Sendhoff
IPC分类号： G06F15/18 , G06F5/00
CPC分类号： G06N3/126
摘要： The invention relates to an evolutionary optimization method. First, an initial population of individuals is set up and an original fitness function is applied. Then the offspring individuals having a high evaluated quality value as parents are selected. In a third step, the parents are reproduced to create a plurality of offspring individuals. The quality of the offspring individuals is evaluated selectively using an original fitness function or an approximate fitness function. Finally, the method returns to the selection step until a termination condition is met. The step of evaluating the quality of the offspring individuals includes grouping all offspring individuals in clusters, selecting for each cluster one or a plurality of offspring individuals, resulting in altogether selected offspring individuals, evaluating the selected offspring individuals by the original fitness function, and evaluating the remaining offspring individuals by means of the approximate fitness function.
摘要翻译：本发明涉及进化优化方法。首先，建立初始人口群体，并应用原始适应度函数。然后选择具有较高评估质量值的后代个体作为父母。第三步，复制父母以创造多个后代个人。使用原始适应度函数或近似适应度函数选择性地评估后代个体的质量。最后，该方法返回到选择步骤，直到满足终止条件。评估后代个体的质量的步骤包括将所有后代个体分组，为每个群体选择一个或多个后代个体，导致完全选择的后代个体，通过原始适应度函数评估所选择的后代个体，以及评估剩余的后代个体通过近似适应度函数。

4. 发明申请

US20070174221A1 Combining Model-Based and Genetics-Based Offspring Generation for Multi-Objective Optimization Using a Convergence Criterion 失效
标题翻译：结合基于模型和遗传学的后代生成多目标优化使用收敛标准
公开(公告)号：US20070174221A1
公开(公告)日：2007-07-26
申请号：US11623639
申请日：2007-01-16
申请人： Bernhard Sendhoff , Yaochu Jin , Edward Tsang , Qingfu Zhang , Aimin Zhou
发明人： Bernhard Sendhoff , Yaochu Jin , Edward Tsang , Qingfu Zhang , Aimin Zhou
IPC分类号： G06F15/18
CPC分类号： G06N3/126 , G06F2217/08
摘要： A system and method for combining the model-based and genetics-based methods are combined according to a convergence criterion. When the population is not converged, the genetics-based approach is used, and when the population is converged, the model-based method is used to generate offspring. The algorithm benefits from using a model-based offspring generation only when the population shows a certain degree of regularity, i.e., converged in a stochastic sense. In addition, a more sophisticated method to construct the stochastic part of the model can be used. Also a biased Gaussian noise (the mean of the noise is not zero), as well as a white Gaussian noise (the mean of the noise is zero) can be preferably used for the stochastic part of the model.
摘要翻译：将基于模型和基于遗传学的方法组合的系统和方法根据收敛标准进行组合。当人口不融合时，使用基于遗传学的方法，当人口聚合时，使用基于模型的方法来产生后代。只有当人口显示一定程度的规律性，即以随机意义收敛时，该算法才有益于使用基于模型的后代。此外，可以使用更复杂的构建模型的随机部分的方法。偏置的高斯噪声（噪声的平均值也不是零），以及白高斯噪声（噪声的平均值为零）也可以优选地用于模型的随机部分。

5. 发明申请

US20050177530A1 Fuzzy preferences in multi-objective optimization (moo) 失效
标题翻译：模糊偏好在多目标优化（moo）
公开(公告)号：US20050177530A1
公开(公告)日：2005-08-11
申请号：US10501378
申请日：2002-12-10
申请人： Yaochu Jin , Bernhard Sendhoff
发明人： Yaochu Jin , Bernhard Sendhoff
IPC分类号： G06N3/00 , G06N3/12 , G06N5/00
CPC分类号： G06N3/126 , G06F2217/08 , Y10S706/913
摘要： A method to obtain the Pareto solutions that are specified by human preferences is suggested. The main idea is to convert the fuzzy preferences into interval-based weights. With the help of the dynamically-weighted aggregation method, it is shown to be successful to find the preferred solutions on two test functions with a convex Pareto front. Compared to the method described in “Use of Preferences for GA-based Multi-Objective Optimization” (Proceedings of 1999 Genetic and Evolutionary Computation Conference, pp. 1504-1510, 1999) by Cvetkovic et al., the method according to the invention is able to find a number of solutions instead of only one, given a set of fuzzy preferences over different objectives. This is consistent with the motivation of fuzzy logic.
摘要翻译：建议一种获得人类偏好指定的帕累托解的方法。主要思想是将模糊偏好转换为基于区间的权重。在动态加权聚合方法的帮助下，显示了在具有凸Pareto前端的两个测试功能上找到首选解决方案。与Cvetkovic等人的“基于GA的多目标优化的使用优选”（1999年遗传与进化计算会议记录，第1504-1510页，1999）中描述的方法相比，本发明的方法是能够找到一些解决方案而不是只有一个，给出一组模糊偏好不同的目标。这与模糊逻辑的动机是一致的。

6. 发明授权

US07739206B2 Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion 失效
标题翻译：结合基于模型和基于遗传学的后代一代用于多目标优化使用收敛标准
公开(公告)号：US07739206B2
公开(公告)日：2010-06-15
申请号：US11623639
申请日：2007-01-16
申请人： Bernhard Sendhoff , Yaochu Jin , Edward Tsang , Qingfu Zhang , Aimin Zhou
发明人： Bernhard Sendhoff , Yaochu Jin , Edward Tsang , Qingfu Zhang , Aimin Zhou
IPC分类号： G06F15/18 , G06N3/00 , G06N3/12
CPC分类号： G06N3/126 , G06F2217/08
摘要： A system and method for combining the model-based and genetics-based methods are combined according to a convergence criterion. When the population is not converged, the genetics-based approach is used, and when the population is converged, the model-based method is used to generate offspring. The algorithm benefits from using a model-based offspring generation only when the population shows a certain degree of regularity, i.e., converged in a stochastic sense. In addition, a more sophisticated method to construct the stochastic part of the model can be used. Also a biased Gaussian noise (the mean of the noise is not zero), as well as a white Gaussian noise (the mean of the noise is zero) can be preferably used for the stochastic part of the model.
摘要翻译：将基于模型和基于遗传学的方法组合的系统和方法根据收敛标准进行组合。当人口不融合时，使用基于遗传学的方法，当人口聚合时，使用基于模型的方法来产生后代。只有当人口显示一定程度的规律性，即以随机意义收敛时，该算法才有益于使用基于模型的后代。此外，可以使用更复杂的构建模型的随机部分的方法。偏置的高斯噪声（噪声的平均值也不是零），以及白高斯噪声（噪声的平均值为零）也可以优选地用于模型的随机部分。

7. 发明授权

US07383236B2 Fuzzy preferences in multi-objective optimization (MOO) 失效
标题翻译：模糊偏好在多目标优化（MOO）
公开(公告)号：US07383236B2
公开(公告)日：2008-06-03
申请号：US10501378
申请日：2002-12-10
申请人： Yaochu Jin , Bernhard Sendhoff
发明人： Yaochu Jin , Bernhard Sendhoff
IPC分类号： G06F15/18 , G06N3/00 , G06N3/12
CPC分类号： G06N3/126 , G06F2217/08 , Y10S706/913
摘要： A method to obtain the Pareto solutions that are specified by human preferences is suggested. The main idea is to convert the fuzzy preferences into interval-based weights. With the help of the dynamically-weighted aggregation method, it is shown to be successful to find the preferred solutions on two test functions with a convex Pareto front. Compared to the method described in “Use of Preferences for GA-based Multi-Objective Optimization” (Proceedings of 1999 Genetic and Evolutionary Computation Conference, pp. 1504-1510, 1999) by Cvetkovic et al., the method according to the invention is able to find a number of solutions instead of only one, given a set of fuzzy preferences over different objectives. This is consistent with the motivation of fuzzy logic.
摘要翻译：建议一种获得人类偏好指定的帕累托解的方法。主要思想是将模糊偏好转换为基于区间的权重。在动态加权聚合方法的帮助下，显示了在具有凸Pareto前端的两个测试功能上找到首选解决方案。与Cvetkovic等人的“基于GA的多目标优化的使用优选”（1999年遗传与进化计算会议记录，第1504-1510页，1999）中描述的方法相比，本发明的方法是能够找到一些解决方案而不是只有一个，给出一组模糊偏好不同的目标。这与模糊逻辑的动机是一致的。

8. 发明申请

US20050209982A1 Reduction of fitness evaluations using clustering techniques and neural network ensembles 失效
标题翻译：使用聚类技术和神经网络集合减少健身评估
公开(公告)号：US20050209982A1
公开(公告)日：2005-09-22
申请号：US11042991
申请日：2005-01-24
申请人： Yaochu Jin , Bernhard Sendhoff
发明人： Yaochu Jin , Bernhard Sendhoff
IPC分类号： G06N3/00 , G06N3/12 , G06F15/18
CPC分类号： G06N3/126
摘要： One embodiment of the invention proposes an evolutionary optimization method. In a first step, an initial population of individuals is set up and an original fitness function is applied. Then the offspring individuals having a high evaluated quality value as parents are selected. In a third step, the parents are reproduced to create a plurality of offspring individuals. The quality of the offspring individuals is evaluated by means of a fitness function, wherein selectively the original or an approximate fitness function is used. Finally, the method goes back to the selection step until a termination condition is met. According to an embodiment, the step of evaluating the quality of the offspring individuals consists in grouping all λ offspring individuals in clusters, selecting for each cluster one or a plurality of offspring individuals, resulting in altogether ξ selected offspring individuals, evaluating the ξ selected offspring individuals by means of the original fitness function, and evaluating the remaining λ-ξ offspring individuals by means of the approximate fitness function.
摘要翻译：本发明的一个实施例提出了一种进化优化方法。在第一步中，建立个体的初始群体并应用原始适应度函数。然后选择具有较高评估质量值的后代个体作为父母。第三步，复制父母以创造多个后代个人。通过适应度函数评估后代个体的质量，其中选择性地使用原始或近似适应度函数。最后，该方法返回到选择步骤，直到满足终止条件。根据一个实施例，评估后代个体的质量的步骤在于将所有λ后代个体分成群集，为每个群体选择一个或多个后代个体，导致一共xi个选择的后代个体，评估所选择的后代通过原始适应度函数的个体，并且通过近似适应度函数来评估剩余的λ-xi后代个体。

9. 发明授权

US07363280B2 Methods for multi-objective optimization using evolutionary algorithms 失效
标题翻译：使用进化算法进行多目标优化的方法
公开(公告)号：US07363280B2
公开(公告)日：2008-04-22
申请号：US10007906
申请日：2001-11-09
申请人： Yaochu Jin , Bernhard Sendhoff
发明人： Yaochu Jin , Bernhard Sendhoff
IPC分类号： G06F15/18 , G06F17/00 , G06N3/00 , G06N3/12 , G06N5/00
CPC分类号： G06Q10/04
摘要： In the field of multi-objective optimization using evolutionary algorithms conventionally different objectives are aggregated and combined into one objective function using a fixed weight when more than one objective needs to be optimized. With such a weighted aggregation, only one solution can be obtained in one run. Therefore, according to the present invention two methods to change the weights systematically and dynamically during the evolutionary optimization are proposed. One method is to assign uniformly distributed weight to each individual in the population of the evolutionary algorithm. The other method is to change the weight periodically when the evolution proceeds. In this way a full set of Pareto solutions can be obtained in one single run.
摘要翻译：在使用进化算法的多目标优化领域中，当需要优化多于一个的目标时，常规的不同目标被聚合并组合成一个目标函数，使用固定权重。通过这种加权聚合，一次运行中只能得到一个解决方案。因此，根据本发明，提出了在进化优化期间系统地和动态地改变权重的两种方法。一种方法是对进化算法的群体中的每个个体赋予均匀分布的权重。另一种方法是在进化过程中周期性地改变重量。以这种方式，在一次运行中可以获得一整套帕累托解决方案。

10. 发明授权

US07043462B2 Approximate fitness functions 失效
公开(公告)号：US07043462B2
公开(公告)日：2006-05-09
申请号：US10007734
申请日：2001-11-09
申请人： Yaochu Jin , Bernhard Sendhoff
发明人： Yaochu Jin , Bernhard Sendhoff
IPC分类号： G06F15/18
CPC分类号： G06N3/126
摘要： A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization that is able to guarantee the correct convergence of the evolutionary algorithm and to reduce the computation costs as much as possible. Control of the evolution and updating of the approximate models are based on the estimated fidelity of the approximate model. The frequency at which the original function is called and the approximate model is updated is determined by the local fidelity of the approximate model. By local fidelity the fidelity of the model for the region where the current population is located is designated. The lower the model fidelity is, the more frequently the original function should be called and the approximate models should be updated.

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