16733583
Evolving Artificial Neural Networks through Evolutionary Programming
A tool for micro-soldering the connecting tags of an integrated circuit chip to corresponding terminals on a substrate by Joule heating includes a high conductivity bit having a planar bottom face adapted to contact the tags and press them against the terminals while applying sufficient heat to the tags to solder them to the terminals. The face has a perimeter and a centrally apertured portion adapted to receive and accommodate the circuit chip. The apertured portion forms a geometric loop on the planar face having opposite sides spaced from each other and adapted to contact the tags of the chip. A continuous high electrical conductivity flange extends upwardly from all portions of the perimeter of the face to provide rigidity to the bit. A pair of high conductivity strips extend from facing segments of the flange for applying current to and removing current from the flange on opposite sides of the loop so that a pair of symmetrical current half loops extend about the face between the opposite sides thereof. Each strip has a reduced cross-sectional area symmetrical with the side of the face and removed from the intersection of the strip with the flange.
18179447
Towards Designing Neural Network Ensembles by Evolution
This paper proposes a co-evolutionary learning system, i.e., CELS, to design neural network (NN) ensembles. CELS addresses the issue of automatic determination of the number of individual NNs in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of CELS is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. Experiments on two real-world problems demonstrate that CELS can produce NN ensembles with good generalisation ability.
13208870
An Evolutionary Approach to Adaptive Model-Building
Learning an input-output mapping from a set of examples can be regarded as a problem of model-building. From this point of view, by a hierarchical representation scheme for models, an evolutionary computational approach to model-building problems is proposed in this paper, which is based on the ideas from the evolution programs and the genetic programmings. The computer experiments indicate that it is surprisingly effective in searching the optimal model for model-building problems.
45330274
Time Series Prediction by Using Negatively Correlated Neural Networks
Negatively correlated neural networks (NCNNs) have been proposed to design neural network (NN) ensembles [1]. The idea of NCNNs is to encourage different individual NNs in the ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. In this paper, NCNNs are applied to two time series prediction problems (i.e., the Mackey-Glass differential equation and the chlorophyll-a prediction in Lake Kasumigaura). The experimental results show that NCNNs can produce NN ensembles with good generalisation ability.
9804135
EPNet for Chaotic Time-Series Prediction
EPNet is an evolutionary system for automatic design of artificial neural networks (ANNs) [1, 2, 3]. Unlike most previous methods on evolving ANNs, EPNet puts its emphasis on evolving ANN'S behaviours rather than circuitry. The parsimony of evolved ANNs is encouraged by the sequential application of architectural mutations. In this paper, EP Net is applied to a couple of chaotic time-series prediction problems (i.e., the Mackey-Glass differential equation and the logistic map). The experimental results show that EPNet can produce very compact ANNs with good prediction ability in comparison with other algorithms.
16112032
Fast Evolution Strategies
Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming
5302961
A Group Theoretic Formalization of Surface Contact
The surface contacts between solids are always associated with a set of symmetries of the contacting surfaces. These symmetries form a group known as the symmetry group of the surface. In this article we develop a group theoretic formalization for describing surface contact between solids. In particular we define (1) primitive and compound features of a solid, (2) a topological characterization of these features, and (3) the symmetry groups of primitive and compound features. The symmetry group of a feature is a descriptor of the fea ture that is at once abstract and quantitative. We show how to use group theory concepts to describe the exact relative motion (position) of solids under surface contacts, which can be either rigid or articulated. The central result of this article is to prove the following: 1. When primitive features of a solid are mutually dis tinct, 1-congruent or 2-congruent, the symmetry group of a compound feature can be expressed in terms of the intersection of the symmetry groups of its primitive features. 2. When two solids have surface contact, their rela tive positions can be expressed as a coset of their common symmetry group, which in turn can be ex pressed in terms of the intersections of the symmetry groups of the primitive features involved in this con tact. These results show that using group theory to formalize surface contacts is a general approach for specifying spatial relationships and forms a sound basis for the automation of robotic task planning. One advantage of this formulation is its ability to express continuous motions between two surface- contacting solids in a computational manner and to avoid combinatorics arising from multiple relationships, especially from discrete symmetries in the assembly parts and their fea tures. At the end of this article, a geometric representation for symmetry groups and an efficient group intersection algorithm using characteristic invariants are described.
5878659
An optimization algorithm for free-form surface partitioning based on weighted gaussian image
Partitioning free-form surfaces into sub-patches and finding optimal representative normal for each patch to maximize a global objective function is an important two-level operation in diverse industrial applications. In this paper, by solving a maximum hemispherical partitioning problem raised from a weighted Gaussian image, an optimization algorithm is proposed to partition a free-form surface into two sub-patches and simultaneously report the optimal representative normals. By discretizing the free-form surface with W sample points and clustering normals on the surface with m distinct sample normals, the proposed algorithm is designed, in general, with O(m2W2) time complexity and O(W2) space complexity, and in particular, if the surface is convex, in O(m2 logm) time complexity. Case studies with four representative examples are presented and a real world application is exploited to demonstrate the effectiveness and usefulness of the proposed algorithm.
181647
Geometric modeling with conical meshes and developable surfaces
In architectural freeform design, the relation between shape and fabrication poses new challenges and requires more sophistication from the underlying geometry. The new concept of conical meshes satisfies central requirements for this application: They are quadrilateral meshes with planar faces, and therefore particularly suitable for the design of freeform glass structures. Moreover, they possess a natural offsetting operation and provide a support structure orthogonal to the mesh. Being a discrete analogue of the network of principal curvature lines, they represent fundamental shape characteristics. We show how to optimize a quad mesh such that its faces become planar, or the mesh becomes even conical. Combining this perturbation with subdivision yields a powerful new modeling tool for all types of quad meshes with planar faces, making subdivision attractive for architecture design and providing an elegant way of modeling developable surfaces.
14501514
Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key assumption behind this work is that two examples tend to have large overlap in their assigned class memberships if they share high similarity in their input patterns. We capitalize this assumption by first computing two sets of similarities, one based on the input patterns of examples, and the other based on the class memberships of the examples. We then search for the optimal assignment of class memberships to the unlabeled data that minimizes the difference between these two sets of similarities. The optimization problem is formulated as a constrained Non-negative Matrix Factorization (NMF) problem, and an algorithm is presented to efficiently find the solution. Compared to the existing approaches for multi-label learning, the proposed approach is advantageous in that it is able to explore both the unlabeled data and the correlation among different classes simultaneously. Experiments with text categorization show that our approach performs significantly better than several state-of-the-art classification techniques when the number of classes is large and the size of training data is small.
16164675
Energy efficient routing with adaptive data fusion in sensor networks
While in-network data fusion can reduce data redundancy and hence curtail network load, the fusion process itself may introduce significant energy consumption for emerging wireless sensor networks with vectorial data. Therefore, fusion-driven routing protocols for sensor networks cannot optimize over communication cost only -- fusion cost must also be accounted for. Towards this end, we design a novel routing algorithm, called Adaptive Fusion Steiner Tree (AFST), for energy efficient data gathering in sensor networks that jointly optimizes over the costs for data transmission and data fusion. Furthermore, AFST evaluates the benefit and cost of data fusion along information routes and adaptively adjusts whether fusion shall be performed. Analytically and experimentally, we show that AFST achieves better performance than existing algorithms including SLT, MFST, and SPT.