Automating the packing heuristic design process with genetic programming

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains

Exploring Large Digital Library Collections Using a Map-Based Visualisation

In this paper we describe a novel approach for exploring large document collections using a map-based visualisation. We use hierarchically structured semantic concepts that are attached to the documents to create a visualisation of the semantic space that resembles a Google Map. The approach is novel in that we exploit the hierarchical structure to enable the approach to scale to large document collections and to create a map where the higher levels of spatial abstraction have semantic meaning. An informal evaluation is carried out to gather subjective feedback from users. Overall results are positive with users finding the visualisation enticing and easy to use

Exploring the Semantic Structure of Technical Document Collections: A Cooperative Systems Approach

. Identifying and analyzing the knowledge available in document form is a key element of corporate knowledge management. In engineering-intensive organizations, it involves tasks such as standard generation and evaluation, comparison of related cases and experience reuse in their treatment. In this paper, we present the design, implementation, and some application experiences with a modular approach that allows a variety of techniques from semantic document analysis to interoperate with a tailorable map-centered visualization of the structure of technical document collections.

Comparison of smoking behavior change for SI and UC study groups. MRFIT Research Group

BACKGROUND. The results of MRFIT smoking intervention program are presented for the 4,103 special intervention and 4,091 usual care men who reported smoking cigarettes at the first screening visit. RESULTS. Among the special intervention men, the reported cessation rate increased from 43.1% at 12 months to 48.9% at 72 months. The reported cessation rate among the usual care men increased from 13.5% at 12 months to 28.8% at 72 months. Among smokers who reported cessation at 72 months, 51.3% of special intervention men and 22.7% of usual care men had quit smoking within the first year and remained abstinent thereafter. Average thiocyanate and expired-air carbon monoxide served as objective measures of smoking and were significantly lower among the special intervention men than among the usual care men over the entire follow-up period. The reported cessation rates at 72 months varied according to initial levels of smoking. Smokers reporting 1-19 cigarettes per day at entry were more likely to quit than heavier smokers. For each category of smoking at entry (1-19, 20-39, and 40 or more cigarettes per day) significantly more special intervention than usual care smokers reported cessation. CONCLUSION. These results indicate that the MRFIT smoking intervention program was successful in promoting early cigarette smoking cessation and maintaining cessation over the entire trial for a large percentage of cigarette smokers

Predicting reaction times in word recognition by unsupervised learning of morphology

A central question in the study of the mental lexicon is how morphologically complex words are processed. We consider this question from the viewpoint of statistical models of morphology. As an indicator of the mental processing cost in the brain, we use reaction times to words in a visual lexical decision task on Finnish nouns. Statistical correlation between a model and reaction times is employed as a goodness measure of the model. In particular, we study Morfessor, an unsupervised method for learning concatenative morphology. The results for a set of inflected and monomorphemic Finnish nouns reveal that the probabilities given by Morfessor, especially the Categories-MAP version, show considerably higher correlations to the reaction times than simple word statistics such as frequency, morphological family size, or length. These correlations are also higher than when any individual test subject is viewed as a model