16619809
Two-Step Genetic Programming for Optimization of RNA Common-Structure
We present an algorithm for identifying putative non-coding RNA (ncRNA) using an RCSG (RNA Common-Structural Grammar) and show the effectiveness of the algorithm. The algorithm consists of two steps: structure learning step and sequence learning step. Both steps are based on genetic programming. Generally, genetic programming has been applied to learning programs automatically, reconstructing networks, and predicting protein secondary structures. In this study, we use genetic programming to optimize structural grammars. The structural grammars can be formulated as rules of tree structure including function variables. They can be learned by genetic programming. We have defined the rules on how structure definition grammars can be encoded into function trees. The performance of the algorithm is demonstrated by the results obtained from the experiments with RCSG of tRNA and 5S small RNA.
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.
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.
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.
16669804
An effective region-based image retrieval framework
We present a region-based image retrieval framework that integrates efficient region-based representation in terms of storage and retrieval and effective on-line learning capability. The framework consists of methods for image segmentation and grouping, indexing using modified inverted file, relevance feedback, and continuous learning. By exploiting a vector quantization method, a compact region-based image representation is achieved. Based on this representation, an indexing scheme similar to the inverted file technology is proposed. In addition, it supports relevance feedback based on the vector model with a weighting scheme. A continuous learning strategy is also proposed to enable the system to self improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.
6883417
Fast k-nearest neighbor classification using cluster-based trees
Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.
28314294
Fast Learning Algorithms for Feedforward Neural Networks
In order to improve the training speed of multilayer feedforward neural networks (MLFNN), we propose and explore two new fast backpropagation (BP) algorithms obtained: (1) by changing the error functions, in case using the exponent attenuation (or bell impulse) function and the Fourier kernel function as alternative functions; and (2) by introducing the hybrid conjugate-gradient algorithm of global optimization for dynamic learning rate to overcome the conventional BP learning problems of getting stuck into local minima or slow convergence. Our experimental results demonstrate the effectiveness of the modified error functions since the training speed is faster than that of existing fast methods. In addition, our hybrid algorithm has a higher recognition rate than the Polak-Ribieve conjugate gradient and conventional BP algorithms, and has less training time, less complication and stronger robustness than the Fletcher-Reeves conjugate-gradient and conventional BP algorithms for real speech data.
10975724
Adding Semantics to Email Clustering
This paper presents a novel algorithm to cluster emails according to their contents and the sentence styles of their subject lines. In our algorithm, natural language processing techniques and frequent itemset mining techniques are utilized to automatically generate meaningful generalized sentence patterns (GSPs) from subjects of emails. Then we put forward a novel unsupervised approach which treats GSPs as pseudo class labels and conduct email clustering in a supervised manner, although no human labeling is involved. Our proposed algorithm is not only expected to improve the clustering performance, it can also provide meaningful descriptions of the resulted clusters by the GSPs. Experimental results on open dataset (Enron email dataset) and a personal email dataset collected by ourselves demonstrate that the proposed algorithm outperforms the K-means algorithm in terms of the popular measurement Fl. Furthermore, the cluster naming readability is improved by 68.5% on the personal email dataset.
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.