In a steady-state evolution, tournament selection traditionally uses the fitness function to select the parents, and negative selection chooses an individual to be replaced with an offspring. This contribution focuses on analyzing the behavior, in terms of performance, of different heuristics when used instead of the fitness function in tournament selection. The heuristics analyzed are related to measuring the similarity of the individuals in the semantic space. In addition, the analysis includes random selection and traditional tournament selection. These selection functions were implemented on our Semantic Genetic Programming system, namely EvoDAG, which is inspired by the geometric genetic operators and tested on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of accuracy and the random selection, in the negative tournament, produces the best combination, and the difference in performances between this combination and the tournament selection is statistically significant. Furthermore, we compare EvoDAG's performance using the selection heuristics against 18 classifiers that included traditional approaches as well as auto-machine-learning techniques. The results indicate that our proposal is competitive with state-of-art classifiers. Finally, it is worth to mention that EvoDAG is available as open source software.
Chen @cite_11 proposed the Angle-Driven Selection (ADS) where the first parent is selected using fitness and the second is with an angle-distance defined as @math . One of our selection heuristics is similar to ADS; however, there are significant differences, the first parent is randomly selected whereas the second parent is selected using an equivalent similarity with the difference that the target behavior is not considered in our approach.
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Geometric semantic genetic programming (GP) has recently attracted much attention. The key innovations are inducing a unimodal fitness landscape in the semantic space and providing a theoretical framework for designing geometric semantic operators. The geometric semantic operators aim to manipulate the semantics of programs by making a bounded semantic impact and generating child programs with similar or better behavior than their parents. These properties are shown to be highly related to a notable generalization improvement in GP. However, the potential ineffectiveness and difficulties in bounding the variations in these geometric operators still limits their positive effect on generalization. This paper attempts to further explore the geometry and search space of geometric operators to gain a greater generalization improvement in GP for symbolic regression. To this end, a new angle-driven selection operator and two new angle-driven geometric search operators are proposed. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, be resistant to overfitting. The experiments show that compared with two state-of-the-art geometric semantic operators, our angle-driven geometric operators not only drive the evolutionary process to fit the target semantics more efficiently but also improve the generalization performance. A further comparison between the evolved models shows that the new method generally produces simpler models with a much smaller size and is more likely to evolve toward the correct structure of the target models. We propose a hybrid algorithm for finding a set of nondominated solutions of a multi objective optimization problem. In the proposed algorithm, a local search procedure is applied to each solution (i.e., each individual) generated by genetic operations. Our algorithm uses a weighted sum of multiple objectives as a fitness function. The fitness function is utilized when a pair of parent solutions are selected for generating a new solution by crossover and mutation operations. A local search procedure is applied to the new solution to maximize its fitness value. One characteristic feature of our algorithm is to randomly specify weight values whenever a pair of parent solutions are selected. That is, each selection (i.e., the selection of two parent solutions) is performed by a different weight vector. Another characteristic feature of our algorithm is not to examine all neighborhood solutions of a current solution in the local search procedure. Only a small number of neighborhood solutions are examined to prevent the local search procedure from spending almost all available computation time in our algorithm. High performance of our algorithm is demonstrated by applying it to multi objective flowshop scheduling problems. Genetic Programming (GP) is an evolutionary method for generating tree structural programs. Normal subtree crossover in GP randomly selects a crossover point in each parental tree, and offspring are created by exchanging the selected subtrees. In the normal crossover, it is difficult to control the global and local search because the similarity between the subtrees is not considered. In this paper, we propose a new crossover operation based on the semantic distance between the subtrees. We call this operation Semantic Control Crossover. By using the Semantic Control Crossover, the global search can be performed in the early stage of search, and the search property can be shifted to the local search as the search proceeds. As the results of experiments, the Semantic Control Crossover showed better performance than the conventional crossover. We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here. We consider the problem of classification using similarity distance functions over data. Specifically, we propose a framework for defining the goodness of a (dis)similarity function with respect to a given learning task and propose algorithms that have guaranteed generalization properties when working with such good functions. Our framework unifies and generalizes the frameworks proposed by [1] and [2]. An attractive feature of our framework is its adaptability to data - we do not promote a fixed notion of goodness but rather let data dictate it. We show, by giving theoretical guarantees that the goodness criterion best suited to a problem can itself be learned which makes our approach applicable to a variety of domains and problems. We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria. We then provide a novel diversity based heuristic to perform task-driven selection of landmark points instead of random selection. We demonstrate the effectiveness of our goodness criteria learning method as well as the landmark selection heuristic on a variety of similarity-based learning datasets and benchmark UCI datasets on which our method consistently outperforms existing approaches by a significant margin. We consider the problem of classification using similarity distance functions over data. Specifically, we propose a framework for defining the goodness of a (dis)similarity function with respect to a given learning task and propose algorithms that have guaranteed generalization properties when working with such good functions. Our framework unifies and generalizes the frameworks proposed by [Balcan-Blum ICML 2006] and [ ICML 2007]. An attractive feature of our framework is its adaptability to data - we do not promote a fixed notion of goodness but rather let data dictate it. We show, by giving theoretical guarantees that the goodness criterion best suited to a problem can itself be learned which makes our approach applicable to a variety of domains and problems. We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria. We then provide a novel diversity based heuristic to perform task-driven selection of landmark points instead of random selection. We demonstrate the effectiveness of our goodness criteria learning method as well as the landmark selection heuristic on a variety of similarity-based learning datasets and benchmark UCI datasets on which our method consistently outperforms existing approaches by a significant margin. For the first time in this paper we present results showing the effect of speaker head pose angle on automatic lip-reading performance over a wide range of closely spaced angles. We analyse the effect head pose has upon the features themselves and show that by selecting coefficients with minimum variance w.r.t. pose angle, recognition performance can be improved when train-test pose angles differ. Experiments are conducted using the initial phase of a unique multi view Audio-Visual database designed specifically for research and development of pose-invariant lip-reading systems. We firstly show that it is the higher order horizontal spatial frequency components that become most detrimental as the pose deviates. Secondly we assess the performance of different feature selection masks across a range of pose angles including a new mask based on Minimum Cross-Pose Variance coefficients. We report a relative improvement of 50 in Word Error Rate when using our selection mask over a common energy based selection during profile view lip-reading. This paper focuses on user adaptation in Spoken Dialogue Systems. It is considered that the system has already been optimised with Reinforcement Learning methods for a set of users. The goal is to use and transfer this prior knowledge to adapt the system to a new user as quickly as possible without impacting asymptotic performance. The first contribution is a source selection method using a multi-armed stochastic bandit algorithm in order to improve the jumpstart, the average performance at the start of the learning curve. Contrarily to previous source selection methods, there is no need to define a metric between users, and it is parameter free. The second contribution is an innovative method for selecting the most informative transitions within the previously selected source, to improve the target model, in such a way that only transitions that were not observed with the target user are transferred from the selected source. For our experimentation, Reinforcement Learning is performed with the Fitted @math -Iteration algorithm. Both methods are validated on a negotiation game: an appointment scheduling simulator that allows the definition of simulated user models adopting diversified behaviours. Compared to state-of-the-art transfer algorithms, results show significant improvements for both jumpstart and asymptotic performance. A fast reference frame selection algorithm for the HEVC encoder is proposed.A relationship between the content similarity and reference frame selection is derived.The content similarity is studied without any extra computational complexity.Experimental results show that the proposed algorithm efficiently removes the encoding complexity of the best reference frame decision process. The high efficiency video coding (HEVC) is the state-of-the-art video coding standard, which achieves about 50 bit rate saving while maintaining the same visual quality as compared to the H.264 AVC. This achieved coding efficiency benefits from a set of advanced coding tools, such as the multiple reference frames (MRF) based interframe prediction, which efficiently improves the coding efficiency of the HEVC encoder, while it also increases heavy computation into the HEVC encoder. The high encoding complexity becomes a bottleneck for the high definition videos and HEVC encoder to be widely used in real-time and low power multimedia applications. In this paper, we propose a content similarity based fast reference frame selection algorithm for reducing the computational complexity of the multiple reference frames based interframe prediction. Based the large content similarity between the parent prediction unit (Inter_2N2N) and the children prediction units (Inter_2NN, Inter_N2N, Inter_NN, Inter_2NnU, Inter_2NnD, Inter_nL2N, and Inter_nR2N), the reference frame selection information of the children prediction units are obtained by learning the results of their parent prediction unit. Experimental results show that the proposed algorithm can reduce about 54.29 and 43.46 MRF encoding time saving for the low-delay-main and random-access-main coding structures, respectively, while the rate distortion performance degradation is negligible. We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (LSTD) algorithm, provide a method for learning the parameters of the value function, but when the number of features is large this algorithm can over-fit to the data and is computationally expensive. In this paper, we propose a regularization framework for the LSTD algorithm that overcomes these difficulties. In particular, we focus on the case of l1 regularization, which is robust to irrelevant features and also serves as a method for feature selection. Although the l1 regularized LSTD solution cannot be expressed as a convex optimization problem, we present an algorithm similar to the Least Angle Regression (LARS) algorithm that can efficiently compute the optimal solution. Finally, we demonstrate the performance of the algorithm experimentally. We consider the problem of retrieving the database points nearest to a given hyperplane query without exhaustively scanning the entire database. For this problem, we propose two hashing-based solutions. Our first approach maps the data to 2-bit binary keys that are locality sensitive for the angle between the hyperplane normal and a database point. Our second approach embeds the data into a vector space where the euclidean norm reflects the desired distance between the original points and hyperplane query. Both use hashing to retrieve near points in sublinear time. Our first method's preprocessing stage is more efficient, while the second has stronger accuracy guarantees. We apply both to pool-based active learning: Taking the current hyperplane classifier as a query, our algorithm identifies those points (approximately) satisfying the well-known minimal distance-to-hyperplane selection criterion. We empirically demonstrate our methods' tradeoffs and show that they make it practical to perform active selection with millions of unlabeled points. There is an urgent need to organize and manage images of people automatically due to the recent explosion of such data on the Web in general and in social media in particular. Beyond face detection and face recognition, which have been extensively studied over the past decade, perhaps the most interesting aspect related to human-centered images is the relationship of people in the image. In this work, we focus on a novel solution to the latter problem, in particular the kin relationships. To this end, we constructed two databases: the first one named UB KinFace Ver2.0, which consists of images of children, their young parents and old parents, and the second one named FamilyFace. Next, we develop a transfer subspace learning based algorithm in order to reduce the significant differences in the appearance distributions between children and old parents facial images. Moreover, by exploring the semantic relevance of the associated metadata, we propose an algorithm to predict the most likely kin relationships embedded in an image. In addition, human subjects are used in a baseline study on both databases. Experimental results have shown that the proposed algorithms can effectively annotate the kin relationships among people in an image and semantic context can further improve the accuracy. We tackle the challenge of kinship verification using novel feature extraction and selection methods, automatically classifying pairs of face images as “related” or “unrelated” (in terms of kinship). First, we conducted a controlled online search to collect frontal face images of 150 pairs of public figures and celebrities, along with images of their parents or children. Next, we propose and evaluate a set of low-level image features for this classification problem. After selecting the most discriminative inherited facial features, we demonstrate a classification accuracy of 70.67 on a test set of image pairs using K-Nearest-Neighbors. Finally, we present an evaluation of human performance on this problem. The wealth of structured (e.g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence. So far, integration of these two different modalities is a difficult process, involving many decisions concerning how best to represent the information so that it will be captured or useful, and hand-labeling large amounts of data. DeepType overcomes this challenge by explicitly integrating symbolic information into the reasoning process of a neural network with a type system. First we construct a type system, and second, we use it to constrain the outputs of a neural network to respect the symbolic structure. We achieve this by reformulating the design problem into a mixed integer problem: create a type system and subsequently train a neural network with it. In this reformulation discrete variables select which parent-child relations from an ontology are types within the type system, while continuous variables control a classifier fit to the type system. The original problem cannot be solved exactly, so we propose a 2-step algorithm: 1) heuristic search or stochastic optimization over discrete variables that define a type system informed by an Oracle and a Learnability heuristic, 2) gradient descent to fit classifier parameters. We apply DeepType to the problem of Entity Linking on three standard datasets (i.e. WikiDisamb30, CoNLL (YAGO), TAC KBP 2010) and find that it outperforms all existing solutions by a wide margin, including approaches that rely on a human-designed type system or recent deep learning-based entity embeddings, while explicitly using symbolic information lets it integrate new entities without retraining. Publisher Summary This chapter focuses on individual differences in human–computer interaction. Differences among users have not been a major concern of commercial computer interface designers. Even behavioral scientists usually select narrowly defined user samples to minimize experimental error when comparing the mean performance of different systems. Those behavioral studies that have analyzed differences among users often have produced descriptive results rather than prescriptions for interface design. In the future, interface designers should focus a great deal of attention on the differences among potential users for three reasons. First, individual differences usually play a major role in determining whether humans can use a computer to perform a job effectively. Second, personnel selection testing, the standard solution to problems of job-related individual differences, cannot be applied to many settings where humans interact with computers. The third reason for designers to be concerned with individual differences is that the technology has reached the point where it is possible to accommodate more user differences. A parallel version of a new hybrid genetic algorithm for the vehicle routing problem with time windows is presented. The route-directed hybrid genetic approach is based upon the simultaneous evolution of two populations of solutions focusing on separate objectives subject to temporal constraint relaxation. While the first population evolves individuals to minimize total traveled distance the second aims at minimizing temporal constraint violation to generate a feasible solution. Genetic operators have been designed to capture key concepts from successful routing techniques to further enhance search diversification and intensification. A master–slave message-passing paradigm characterizes the parallel procedure. The master component controls the execution of the algorithm, coordinates genetic operations and handles parent selection while the slave elements concurrently execute reproduction and mutation operators. Providing additional speed-up, the parallel algorithm further expands on its sequential counterpart, matching or even improving solution quality. Computational results show the proposed technique to be very competitive with the best-known heuristic routing procedures providing some new best-known solutions. Because of the inevitable impact factors such as pose, expression, lighting and aging on faces, identity verification through faces is still an unsolved problem. Research on biometrics raises an even challenging problem--is it possible to determine the kinship merely based on face images? A critical observation that faces of parents captured while they were young are more alike their children's compared with images captured when they are old has been revealed by genetics studies. This enlightens us the following research. First, a new kinship database named UB KinFace composed of child, young parent and old parent face images is collected from Internet. Second, an extended transfer subspace learning method is proposed aiming at mitigating the enormous divergence of distributions between children and old parents. The key idea is to utilize an intermediate distribution close to both the source and target distributions to bridge them and reduce the divergence. Naturally the young parent set is suitable for this task. Through this learning process, the large gap between distributions can be significantly reduced and kinship verification problem becomesmore discriminative. Experimental results show that our hypothesis on the role of young parents is valid and transfer learning is effective to enhance the verification accuracy. This paper proposes several approaches to improve the collision attack on MD4 proposed First, we propose a new local collision that is the best for the MD4 collision attack. Selection of a good message difference is the most important step in achieving effective collision attacks. This is the first paper to introduce an improvement to the message difference approach of , where we propose a new local collision. Second, we propose a new algorithm for constructing differential paths. While similar algorithms have been proposed, they do not support the new local collision technique. Finally, we complete a collision attack, and show that the complexity is smaller than the previous best work. This descriptive study of millions of US Facebook users documents "friending" and communication patterns, exploring parent-child relationships across a variety of life stages and gender combinations. Using statistical techniques on 400,000 posts and comments, we identify differences in how parents talk to their children (giving advice, affection, and reminders to call) compared to their other friends, and how they address their adult sons and daughters (talking about grandchildren and health concerns, and linguistically treating them like peers) compared to their teenage children. Parents and children have 20-30 mutual friends on the site, 19 of whom are relatives. Unlike previous findings on family communication, interaction frequency on Facebook does not decrease with geographic distance. For high-dimensional data, this work proposes two novel visual exploration methods to gain insights into the data aspect and the dimension aspect of the data. The first is a Dimension Projection Matrix, as an extension of a scatterplot matrix. In the matrix, each row or column represents a group of dimensions, and each cell shows a dimension projection (such as MDS) of the data with the corresponding dimensions. The second is a Dimension Projection Tree, where every node is either a dimension projection plot or a Dimension Projection Matrix. Nodes are connected with links and each child node in the tree covers a subset of the parent node's dimensions or a subset of the parent node's data items. While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between different combinations of subspaces. Both Dimension Projection Matrix and Dimension Project Tree can be constructed algorithmically through automation, or manually through user interaction. Our implementation enables interactions such as drilling down to explore different levels of the data, merging or splitting the subspaces to adjust the matrix, and applying brushing to select data clusters. Our method enables simultaneously exploring data correlation and dimension correlation for data with high dimensions.