区间适应值交互式遗传算法神经网络代理模型

为了解决交互式遗传算法的用户疲劳问题,提出区间适应值交互式遗传算法神经网络代理模型．首先,对用户已评价个体的基因型及其适应值进行采样以训练神经网络,使其逼近区间适应值的上下限;然后,利用神经网络代理模型,评价后续的部分进化个体,并不断更新训练数据和代理模型,以保证逼近精度;最后,对算法性能进行了定量分析,并将其应用于服装进化设计系统．分析结果表明,所提算法在减轻用户疲劳的前提下,具有更多找到满意解的机会．

     

基于BP神经网络代理模型的交互式遗传算法

针对交互式遗传算法存在用户评价噪声和审美疲劳的问题,提出一种基于误差反向传播神经网络用户认知代理模型的交互式遗传算法。通过构建用户评价噪声模型,形成进化个体动态模糊区间适应值,以刻画用户认知随机不确定性;在用户认知确定阶段历史评价信息基础上,构建误差反向传播神经网络代理模型,给出一种新的适应值估计策略;通过度量均方误差,实现代理模型的管理与更新。将所提方法应用于蜡染风格图案设计,并与其他典型算法对比。结果表明,该方法能够有效优化进化个体适应值质量、降低用户审美疲劳。     

基于半监督学习的变种群规模区间适应值交互式遗传算法

为了减轻用户疲劳并增强算法的搜索性能,本文在变种群规模交互式遗传算法的基础上引入协同训练半监督学习方法,提出基于半监督学习的变种群规模区间适应值交互式遗传算法.根据对大规模种群的聚类结果,给出标记样本和未标记样本的获取方法;结合半监督协同学习器逼近误差的改变,提出高可信度未标记样本的选择策略;采用半监督协同学习机制训练两个径向基函数(RBF)神经网络,构造精度高泛化能力强的代理模型;在进化过程中,利用代理模型估计大种群规模进化个体适应值,并根据估计偏差更新代理模型.算法的理论分析及其在服装进化设计系统中的应用结果说明了算法的有效性.     

自适应分区多代理模型交互式遗传算法

为提高交互式遗传算法的性能.提出一种自适应分区多代理模型交互式遗传算法.该算法基于关键维分割进化初期的搜索空间,同时基于进化进程、逼近精度以及用户评价敏感度,自适应地分割进化中后期的搜索空间.在子空间上,采用多类代理模型学习用户对进化个体评价,并用于评价后续进化的部分或全部个体.将该算法应用于服装进化设计系统,实验结果表明,算法在种群多样性、减轻用户疲劳及用户对优化结果满意度等方面均具有优越性.     

自适应分区多代理模型交互式遗传算法

为提高交互式遗传算法的性能,提出一种自适应分区多代理模型交互式遗传算法&.该算法基于关键维分割进化初期的搜索空间,同时基于进化进程,逼近精度以及用户评价敏感度,自适应地分割进化中后期的搜索空间.在子空间上,采用多类代理模型学习用户对进化个体评价,并用于评价后续进化的部分或全部个体.将该算法应用于服装进化设计系统,实验结果表明,算法在种群多样性,减轻用户疲劳及用户对优化结果满意度等方面均具有优越性.

     

基于区间适应值交互式遗传算法的加权多输出高斯过程代理模型

融合了用户认知和智能评价的交互式遗传算法（Interactive genetic algorithm,IGA）是解决一类定性性能指标优化问题的有效方法,但是,评价不确定性和易疲劳性极大地限制了该算法解决实际问题的能力. 基于用户已评价信息,采用合适的机器学习方法,构建用户认知代理模型是解决上述问题的常用方法之一. 但是,现有研究成果均没有考虑用户评价不确定性对学习样本、代理模型的影响,以及模型拟合不确定性对基于适应值的进化操作有效性的影响. 针对上述问题,本文提出基于加权多输出高斯过程（Gaussian process,GP）代理模型的交互式遗传算法. 首先,在区间适应值评价模式下,提取学习样本的噪声特性,以确定相应学习样本对代理模型的影响度权重系数,构建两输出高斯过程代理模型;然后,利用代理模型提供的预测值及预测置信水平,给出一种新的个体适应值估计方法和个体选择方法;基于模型预测信息,实现模型更新管理. 将所提算法分别应用于含噪函数和服装设计问题中,所得结果表明本文算法可更好地拟合和跟踪用户认知,减小对进化搜索的误导,更快找到用户满意解.     

基于进化个体适应值灰度的自适应交互式遗传算法

为将交互式遗传算法成功应用于复杂优化问题,有必要提高交互式遗传算法的性能。提出基于进化个体适应值灰度的交互式遗传算法,该算法采用灰度衡量进化个体的适应值评价不确定性;通过适应值区间的分析,提取反映进化种群分布的信息;基于此,给出了进化个体的交叉和变异概率。将该算法应用于服装进化设计系统,结果表明该算法在每代可以获取更多的满意解。     

基于CP-nets 的偏好感知交互式遗传算法及其个性化搜索

针对用户显式评价导致用户疲劳,进而限制交互式遗传算法搜索性能的问题,研究基于用户交互行为和条件偏好网络(CP-nets)的隐式评价模式的交互式遗传算法,并将其应用于图书商品个性化搜索。首先,给出用户交互行为的数学描述,建立基于用户少量交互行为的条件偏好网络模型以拟合用户偏好;然后,利用CP-nets模型估计用户对进化个体的评价值,实施进化操作以帮助用户尽快找到满意解。在个性化搜索中的应用验证了所提出算法的有效性。     

基于区间适应值灰度的交互式遗传算法

针对交互式遗传算法缺乏衡量评价的不确定性问题,采用区间数评价进化个体适应值,利用灰度衡量评价的不确定性。通过区间适应值的灰度分析,提取反映种群进化分布的信息,给出进化个体的自适应交叉和变异概率。应用于服装进化设计系统的分析结果表明,该算法可有效缓解人的疲劳,提高优化效率。     

基于个体适应值灰模型的交互式遗传算法

为将交互式遗传算法应用于复杂的优化问题中,提出一种基于进化个体适应值灰模型预测的交互式遗传算法,为每代适应值序列建立灰模型,以衡量个体适应值评价的不确定性,通过对灰模型的灰预测,提取进化个体评价的可信度,在此基础上,给出进化个体适应值修正公式,将该算法应用于服装进化设计系统中。实验结果表明,该算法在每代都能获取更多的满意解。     

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.     

融入神经网络的路径覆盖测试数据进化生成

利用遗传算法生成复杂软件的测试数据,是软件测试领域一个全新的研究方向.传统的基于遗传算法的测试数据生成技术,需要以每个测试数据作为输入运行被测程序,以获得个体的适应值,因此,需要消耗大量的运行时间.为了降低运行程序带来的时间消耗,提出一种基于神经网络的路径覆盖测试数据进化生成方法,主要思想是:首先,利用一定样本训练神经网络,以模拟个体的适应值;在利用遗传算法生成测试数据时,先利用训练好的神经网络粗略计算个体适应值;对适应值较好的优秀个体,再通过运行程序,获得精确的适应值.最后的实验结果表明,该方法可以有效降低运行程序产生的时间消耗,从而提高测试数据生成的效率.     

Building surrogate models for engineering problems by integrating limited simulation data and monotonic engineering knowledge

The use of surrogate models to replace expensive computations with computer simulations has been widely studied in engineering problems. However, often only limited simulation data is available when designing complex products due to the cost of obtaining this kind of data. This presents a challenge for building surrogate models because the information contained in the limited simulation data is incomplete. Therefore, a method for building surrogate models by integrating limited simulation data and engineering knowledge with evolutionary neural networks (eDaKnow) is presented. In eDaKnow, a neural network uses an evolutionary algorithm to integrate the simulation data and the monotonic engineering knowledge to learn its weights and structure synchronously. This method involves converting both limited simulation data and engineering knowledge into the respective fitness functions. Compared with the previous work of others, we propose a method to train the surrogate model by combining data and knowledge through evolutionary neural network. We take knowledge as fitness function to train the model, and use a network structure self-learning method, which means that there is no need to adjust the network structure manually. The empirical results show that: (1) eDaKnow can be used to integrate limited simulation data and monotonic knowledge into a neural network, (2) the prediction accuracy of the newly constructed surrogate model is increased significantly, and (3) the proposed eDaKnow outperforms other methods on relatively complex benchmark functions and engineering problems.     

基于代理模型的差分进化约束优化

针对耗时计算目标函数的约束优化问题,提出用代理模型来代替耗时计算目标函数的方法,并结合目标函数的信息对约束个体进行选择,从而提出基于代理模型的差分进化约束优化算法。首先,采用拉丁超立方采样方法建立初始种群,用耗时计算目标函数对初始种群进行评估,并以此为样本数据建立目标函数的神经网络代理模型。然后,用差分进化方法为种群中的每一个亲本产生后代,并对后代使用代理模型进行评估,采用可行性规则来比较后代与其亲本并更新种群,根据替换机制将种群中较劣的个体替换为备用存档中较优的个体。最后,当达到最大适应度评估次数时算法停止,给出最优解。该算法与对比算法在10个测试函数上运行的结果表明,该算法得出的结果更精确。将该算法应用于工字梁优化问题的结果表明,相较于优化前的算法,该算法的适应度评估次数减少了80%;相对于FROFI（Feasibility Rule with the incorporation of Objective Function Information）算法,该算法的适应度评估次数减少了36%。运用所提算法进行优化可以有效减少调用耗时计算目标函数的次数,提升优化效率,节约计算成本。     

Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use

Information technology has recently become the medium in which much professional office work is performed. This change offers an unprecedented opportunity to observe and record exactly how that work is performed. We describe our observation and logging processes and present an overview of the results of our long-term observations of a number of users of one desktop application. We then present our method of providing individualized instruction to each user by employing a new kind of user model and a new kind of expert model. The user model is based on observing the individual's behavior in a natural environment, while the expert model is based on pooling the knowledge of numerous individuals. Individualized instructional topics are selected by comparing an individual's knowledge to the pooled knowledge of her peers.     

On Model-Based Evolutionary Computation

 Traditional evolutionary computing techniques use an explicit fitness function – mathematical or simulated – to derive a solution to a problem from a population of individuals, over a number of generations. In this paper an approach which allows such techniques to be used on problems in which evaluations are costly, which cannot be expressed formally, or which are difficult to simulate, is examined. A neural network is trained using example individuals with the explicit fitness and the resulting model of the fitness function is then used by the evolutionary algorithm to find a solution. It is shown that the approach is effective over a small range of function types in comparison to the traditional approach when limited training data is available. An iterative step is then added whereby after a number of generations the current best individual in a population is evaluated directly on the explicit fitness function. The individual and its “real” fitness are then added to the training data and the neural network is re-trained to improve its approximation of the fitness function. It is shown that in this way the performance of the model-based architecture is greatly improved on more rugged/complex landscapes without a large increase in the amount of training data required.     

Interactive genetic algorithms with individual’s fuzzy fitness

Interactive genetic algorithms are effective methods to solve an optimization problem with implicit or fuzzy indices, and have been successfully applied to many real-world optimization problems in recent years. In traditional interactive genetic algorithms, many researchers adopt an accurate number to express an individual’s fitness assigned by a user. But it is difficult for this expression to reasonably reflect a user’s fuzzy and gradual cognitive to an individual. We present an interactive genetic algorithm with an individual’s fuzzy fitness in this paper. Firstly, we adopt a fuzzy number described with a Gaussian membership function to express an individual’s fitness. Then, in order to compare different individuals, we generate a fitness interval based on α-cut set, and obtain the probability of individual dominance by use of the probability of interval dominance. Finally, we determine the superior individual in tournament selection with size two based on the probability of individual dominance, and perform the subsequent evolutions. We apply the proposed algorithm to a fashion evolutionary design system, a typical optimization problem with an implicit index, and compare it with two interactive genetic algorithms, i.e., an interactive genetic algorithm with an individual’s accurate fitness and an interactive genetic algorithm with an individual’s interval fitness. The experimental results show that the proposed algorithm is advantageous in alleviating user fatigue and looking for user’s satisfactory individuals.