37126329
Genetic programming of process decomposition strategies for evolvable hardware
Evolvable hardware is able to offer considerably higher performance than general-purpose processors and significantly more flexibility than ASICs. In order to take the advantages of general-purpose processors and ASICs, dividing a complex process into subprocesses is essential. In this paper, we propose a evolutionary method called context switching that splits a task into a set of subtasks whose complexity is manageable on the given hardware. The method is based on genetic programming. Due to its expressive power generic program can represent flexible strategies for decomposing complex tasks. The effectiveness of context switching is demonstrated on the design of adaptive controllers for a team of autonomous mobile robots.
2500417
Evolutionary Induction of Sparse Neural Trees
This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.
16977093
Building Optimal Committees of Genetic Programs
Committee machines are known to improve the performance of individual learners. Evolutionary algorithms generate multiple individuals that can be combined to build committee machines. However, it is not easy to decide how big the committee should be and what members constitute the best committee. In this paper, we present a probabilistic search method for determining the size and members of the committees of individuals that are evolved by a standard GP engine. Applied to a suite of benchmark learning tasks, the GP committees achieved significant improvement in prediction accuracy.
8056566
Bayesian Evolutionary Optimization Using Helmholtz Machines
Recently, several evolutionary algorithms have been proposed that build and use an explicit distribution model of the population to perform optimization. One of the main issues in this class of algorithms is how to estimate the distribution of selected samples. In this paper, we present a Bayesian evolutionary algorithm (BEA) that learns the sample distribution by a probabilistic graphical model known as Helmholtz machines. Due to the generative nature and availability of the wake-sleep learning algorithm, the Helmholtz machines provide an effective tool for modeling and sampling from the distribution of selected individuals. The proposed method has been applied to a suite of GA-deceptive functions. Experimental results show that the BEA with the Helmholtz machine outperforms the simple genetic algorithm.
7514161
An Incremental Subspace Learning Algorithm to Categorize Large Scale Text Data
The dramatic growth in the number and size of on-line information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Inter-class Scatter (IIS) algorithm. Unlike traditional batch learners, IIS learns from a stream of training data, not a set. IIS overcomes the inherent problem of some other incremental operations such as Incremental Principal Component Analysis (PCA) and Incremental Linear Discriminant Analysis (LDA). The experimental results on the synthetic datasets show that IIS performs as well as LDA and is more robust against noise. In addition, the experiments on the Reuters Corpus Volume 1 (RCV1) dataset show that IIS outperforms state-of-the-art Incremental Principal Component Analysis (IPCA) algorithm, a related algorithm, and Information Gain in efficiency and effectiveness respectively.
1990595
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.
5219400
Timer Interaction in Route Flap Damping
Route Flap Damping is a mechanism generally used in network routing protocols. Its goal is to limit the global impact of unstable routes by temporarily suppressing routes with rapid changes over short time periods. Although route damping is a clearly defined and simple procedure at each router, its effect in a large network setting is not well understood. We show that the current damping design leads to the intended behavior only under persistent route flapping. When the number of flaps is small, the global routing dynamics deviates significantly from the expected behavior with a longer convergence delay. Previous work observed that a single route flap can falsely trigger route suppression due to path exploration. However our simulations show that this false suppression only accounts for 30% of the convergence delay after a single route flap. Our study reveals previously unknown interactions between reuse timers at different routers. Route suppression and reuse at different routers are triggered at different times and thus affect the number of updates received by other routers. In turn, this impacts other routers' damping behavior. We propose to use Root Cause Notification to eliminate both false suppression and undesirable timer interaction
16533233
Improving text classification using local latent semantic indexing
Latent semantic indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "local relevancy weighted LSI" to improve text classification by performing a separate single value decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.
734748
Genetic Programming with Active Data Selection
Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting tool for automatic programming and machine learning. One weakness is the enormous time required for evolving complex programs. In this paper we present a method for accelerating evolution speed of genetic programming by active selection of fitness cases during the run. In contrast to conventional genetic programming in which all the given training data are used repeatedly, the presented method evolves programs using only a subset of given data chosen incrementally at each generation. This method is applied to the evolution of collective behaviors for multiple robotic agents. Experimental evidence supports that evolving programs on an incrementally selected subset of fitness cases can significantly reduce the fitness evaluation time without sacrificing generalization accuracy of the evolved programs.