The Sampling-and-Learning Framework: A Statistical View of Evolutionary Algorithms

Evolutionary algorithms (EAs), a large class of general purpose optimization algorithms inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance. This paper presents an attempt towards revealing their general power from a statistical view of EAs. By summarizing a large range of EAs into the sampling-and-learning framework, we show that the framework directly admits a general analysis on the probable-absolute-approximate (PAA) query complexity. We particularly focus on the framework with the learning subroutine being restricted as a binary classification, which results in the sampling-and-classification (SAC) algorithms. With the help of the learning theory, we obtain a general upper bound on the PAA query complexity of SAC algorithms. We further compare SAC algorithms with the uniform search in different situations. Under the error-target independence condition, we show that SAC algorithms can achieve polynomial speedup to the uniform search, but not super-polynomial speedup. Under the one-side-error condition, we show that super-polynomial speedup can be achieved. This work only touches the surface of the framework. Its power under other conditions is still open

Automated Design of Neural Network Architecture for Classification

This Ph.D. thesis deals with finding a good architecture of a neural network classifier. The focus is on methods to improve the performance of existing architectures (i.e. architectures that are initialised by a good academic guess) and automatically building neural networks. An introduction to the Multi-Layer feed-forward neural network is given and the most essential properties for neural networks; there ability to learn from examples is discussion. Topics like traning and generalisation are treated in more explicit. On the basic of this dissuscion methods for finding a good architecture of the network described. This includes methods like; Early stopping, Cross validation, Regularisation, Pruning and various constructions algorithms (methods that successively builds a network). New ideas of combining units with different types of transfer functions like radial basis functions and sigmoid or threshold functions led to the development of a new construction algorithm for classification. The algorithm called "GLOCAL" is fully described. Results from these experiments real life data from a Synthetic Aperture Radar (SAR) are provided.The thesis was written so people from the industry and graduate students who are interested in neural networks hopeful would find it useful.Key words: Neural networks, Architectures, Training, Generalisation deductive and construction algorithms