장병탁 교수

Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this article we investigate the fundamental relationship be

We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function.

We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

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We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical net

Recent interest in human-level intelligence suggests a rethink of the role of machine learning in computational intelligence. We argue that "without cognitive learning the goal of achieving human-level synthetic intelligence is far from completion. Here we review the principles underlying human learning and memory, and identify three of them, i.e., continuity, glocality, and compositionality, as the most fundamental to human-level machine learning. We then propose the recently-developed hypernet

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 a

Document filtering is increasingly deployed in Web environments to reduce information overload of users. We formulate online information filtering as a reinforcement learning problem, i.e., TD(0). The goal is to learn user profiles that best represent information needs and thus maximize the expected value of user relevance feedback. A method is then presented that acquires reinforcement signals automatically by estimating user's implicit feedback from direct observations of browsing behaviors. T

Much previous work on training multilayer neural networks has attempted to speed up the backpropagation algorithm using more sophisticated weight modification rules, whereby all the given training examples are used in a random or predetermined sequence. In this paper we investigate an alternative approach in which the learning proceeds on an increasing number of selected training examples, starting with a small training set. We derive a measure of criticality of examples and present an increment

A Bayesian framework for evolutionary computation is presented. Given a data set for fitness evaluation the best (fittest) individual is defined as the most probable model of the data with respect to the prior knowledge on the problem domain. In each generation, Bayes theorem is used to estimate the posterior fitness of individuals from their prior fitness values. Offspring individuals are then generated by sampling from the posterior distribution combined with the transition probabilities forme

. Selection is an essential component of evolutionary algorithms, playing an important role especially in solving hard optimization problems. Most previous studies on selection have focused on more or less ideal properties based on asymptotic analysis. In this paper, we address the selection problem from a more practical point of view by considering solution quality achievable within acceptable time. The repertoire of methods we compare includes proportional selection, ranking selection, linear

This paper describes the technical approach, hardware design, and software algorithms that have been used by Team THOR in the DARPA Robotics Challenge (DRC) Trials 2013 competition. To overcome big hurdles such as a short development time and limited budget, we focused on forming modular components—in both hardware and software—to allow for efficient and cost‐effective parallel development. The hardware of THOR‐OP (Tactical Hazardous Operations Robot–Open Platform) consists of standardized, adva

A constructive learning algorithm is described that builds a feedforward neural network with an optimal number of hidden units to balance convergence and generalization. The method starts with a small training set and a small network, and expands the training set incrementally after training. If the training does not converge, the network grows incrementally to increase its learning capacity. This process, called selective learning with flexible neural architectures (SELF), results in a construc