43 research outputs found
Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization
A fitness landscape presents the relationship
between individual and its reproductive success in evolutionary
computation (EC). However, discrete and approximate
landscape in an original search space may
not support enough and accurate information for EC
search, especially in interactive EC (IEC). The fitness
landscape of human subjective evaluation in IEC is very
difficult and impossible to model, even with a hypothesis
of what its definition might be. In this paper, we
propose a method to establish a human model in projected
high dimensional search space by kernel classification
for enhancing IEC search. Because bivalent logic
is a simplest perceptual paradigm, the human model
is established by considering this paradigm principle.
In feature space, we design a linear classifier as a human
model to obtain user preference knowledge, which
cannot be supported linearly in original discrete search
space. The human model is established by this method
for predicting potential perceptual knowledge of human.
With the human model, we design an evolution
control method to enhance IEC search. From experimental
evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search
significantly
Speed-up of the R 4 -rule for Distance-Based Neural Network Learning
Abstract-The R 4 -rule is a heuristic algorithm for distancebased neural network (DBNN) learning. Experimental results show that the R 4 -rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R 4 -rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R 4 -rule, we investigate three approaches in this paper. The first one is called the distance preservation (DP) approach, which tries to reduce the number of times for calculating the distance values, and the other two are based on the attentional learning concept, which try to reduce the number of data used for learning. The efficiency of these methods is verified through experiments on several public databases. Index Terms-Distance-based neural networks, nearest neighbor classifiers, neural networks, linear vector quantization, R 4 -rule, attentional learning, pattern recognition. I. INTRODUCTION A distance-Based neural network (DBNN) is a nearest neighbor classifier (NNC) realized in neural network (NN) form. In the literature, DBNN is widely known as selforganizing neural network, though DBNN is a model suitable both for un-supervised learning and for supervised learning [1] - A direct way to design a DBNN is to put all training data into P , with the weight vector of each neuron being one of the training data. Unfortunately, the network so obtained is not efficient if the number of data is large. A more efficient way is to train a small DBNN using the training data, such that |P |, or the number of neurons, is much smaller than the number of data. The question is, how small |P | should be for a given problem. To answer this question, we proposed an algorithm called the R 4 -rule for finding the smallest or nearly smallest DBNN in To reduce the cost of the R 4 -rule, this paper investigates three approaches. The first one is called the distance preservation (DP) approach, which tries to reduce the number of times for calculating the distance values, and the other two are based on the attentional learning concept (AL), which tr