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.
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
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.
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.
39342031
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
Evolutionary programming (EP) has been widely used in numerical optimization in recent years. One of EP's key features is its self-adaptation scheme. In EP, mutation is typically the only operator used to generate new offspring. The mutation is often implemented by adding a random number from a certain distribution (e.g., Gaussian in the case of classical EP) to the parent. An important parameter of the Gaussian distribution is its standard deviation (or equivalently the variance). In the widely used self-adaptation scheme of EP, this parameter is evolved, rather than manually fixed, along with the objective variables. This paper investigates empirically how well the self-adaptation scheme works on a set of benchmark functions. Some anomalies have been observed in the empirical studies, which demonstrate that the self-adaptation scheme may not work as well as hoped for some functions. An experimental evaluation of an existing simple fix to the problem is also carried out in this paper.
15370058
Evolutionary programming made faster
Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In the paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. The paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. The paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
3748150
Mesh blending
A new method for smoothly connecting different patches on triangle meshes with arbitrary connectivity, called mesh blending, is presented. A major feature of mesh blending is to move vertices of the blending region to a virtual blending surface by choosing an appropriate parameterization of those vertices. Once blending is completed, the parameterization optimization is performed to perfect the final meshes. Combining mesh blending with multiresolution techniques, an effective blending technique for meshes is obtained. Our method has several advantages: (1) the user can intuitively control the blending result using different blending radii, (2) the shape of cross-section curves can be adjusted to flexibly design complex models, and (3) the resulting mesh has the same connectivity as the original mesh. In this paper, some examples about smoothing, sharpening, and mesh editing show the efficiency of the method.
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.
7923791
Discontinuity-Based Simplification of Free Form Surface From a Range Image
This paper improves an existing method for more accurate simplification of free form surface represented as dense points. The main improvement lies in two aspects: one is the introduction of a novel transformation of curvatures, the other is to propose sampling points using both curvature information and curvature variation information. The experimental results based on real images show that the original object surface has been better approximated and the majority of the surface discontinuities have been better preserved with considerably few sampled (5%) data points
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.
5373454
A Logic of Limited Belief for Reasoning with Disjunctive Information
The goal of producing a general purpose, semantically motivated, and computationally tractable deductive reasoning service remains surprisingly elusive. By and large, approaches that come equipped with a perspicuous model theory either result in reasoners that are too limited from a practical point of view or fall off the computational cliff. 
 
In this paper, we propose a new logic of belief called SL which lies between the two extremes. We show that query evaluation based on SL for a certain form of knowledge bases with disjunctive information is tractable in the propositional case and decidable in the first-order case. Also, we present a sound and complete axiomatization for propositional SL.