Evolutionary Design of Neural Architectures -- A Preliminary Taxonomy and Guide to Literature

This report briefly motivates current research on evolutionary design of neural architectures (EDNA) and presents a short overview of major research issues in this area. It also includes a preliminary taxonomy of research on EDNA and an extensive bibliography of publications on this topic. The taxonomy is an attempt to categorize current research on EDNA in terms of major research issues addressed and approaches pursued. It is our hope that this will help identify open research questions as well as promising directions for further research on EDNA. The report also includes an appendix that provides some suggestions for effective use of the electronic version of the bibliography

Quantitative Structure-Activity Relationships by Evolved Neural Networks for the Inhibition of Dihydrofolate Reductase by Pyrimidines

Abstract Evolutionary computation provides a useful method for training neural networks in the face of multiple local optima. This paper begins with a description of methods for quantitative structure activity relationships (QSAR). An overview of artificial neural networks for pattern recognition problems such as QSAR is presented and extended with the description of how evolutionary computation can be used to evolve neural networks. Experiments are conducted to examine QSAR for the inhibition of dihydrofolate reductase by pyrimidines using evolved neural networks. Results indicate the utility of evolutionary algorithms and neural networks for the predictive task at hand. Furthermore, results that are comparable or perhaps better than those published previously were obtained using only a small fraction of the previously required degrees of freedom