6336712
Training Feedforward Neural Networks Using Genetic Algorithms
Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some realworld problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. Hence, they are well suited to the problem of training feedforward networks. In this paper, we describe a set of experiments performed on data from a sonar image classification problem. These experiments both 1) illustrate the improvements gained by using a genetic algorithm rather than backpropagation and 2) chronicle the evolution of the performance of the genetic algorithm as we added more and more domain-specific knowledge into it.
2130215
Mapping Classifier Systems Into Neural Networks
Classifier systems are machine learning systems incotporating a genetic algorithm as the learning mechanism. Although they respond to inputs that neural networks can respond to, their internal structure, representation formalisms, and learning mechanisms differ markedly from those employed by neural network researchers in the same sorts of domains. As a result, one might conclude that these two types of machine learning formalisms are intrinsically different. This is one of two papers that, taken together, prove instead that classifier systems and neural networks are equivalent. In this paper, half of the equivalence is demonstrated through the description of a transformation procedure that will map classifier systems into neural networks that are isomorphic in behavior. Several alterations on the commonly-used paradigms employed by neural network researchers are required in order to make the transformation work. These alterations are noted and their appropriateness is discussed. The paper concludes with a discussion of the practical import of these results, and with comments on their extensibility.
6107661
Job Shop Scheduling with Genetic Algorithms
5088549
Mapping Neural Networks into Classifier Systems
2752879
Applying Adaptive Algorithms to Epistatic Domains
John Holland has shown that when adaptive algorithms are used to search certain kinds of extremely large problem spaces, they will converge on a "good" solution fairly quickly. Such problem spaces are characterized by a low degree of epistasis. A host of classical search problems, however, are epistatic in nature. The present paper describes some new techniques for applying adaptive algorithms to epistatic domains, while retaining some of the strength of Holland's convergence proof. These techniques are described for two-dimensional bin-packing problems, and summarized for graph coloring problems. What makes these problems amenable to an adaptive approach is a two-stage evaluation procedure. Encodings of solutions are mutated and reproduced as they are in non-epistatic domains, but their evaluation is carried out after a decoding process. Using the techniques described, convergence is promoted in two ways: one of the natural mutation operators is a weaker version of Holland's crossover, and domain knowledge may be built into decoding processes so that the size of the search space is radically cut down.
18536501
A Hybrid Genetic Algorithm for Classification
In this paper we describe a method for hybridizing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algorithm to classify new data records based on their weighted distance from the members of the training set. We applied our hybrid algorithm to three test cases. Classification results obtained with the hybrid algorithm exceed the performance of the k nearest neighbors algorithm in all three cases.
36571677
Monitoring Human Activity using Computer Vision