We present a new feature selection algorithm for structure-activity and structure-property correlation based on particle swarms. Particle swarms explore the search space through a population of individuals that adapt by returning stochastically toward previously successful regions, influenced by the success of their neighbors. This method, which was… (More)
Application of genetic algorithms to problems where the fitness landscape changes dynamically is a challenging problem. Genetic algorithms for such environments must maintain a diverse population that can adapt to the changing landscape and locate better solutions dynamically. A niching genetic algorithm suitable for locating multiple solutions in a… (More)
Despite their growing popularity among neural network practitioners, ensemble methods have not been widely adopted in structure-activity and structure-property correlation. Neural networks are inherently unstable, in that small changes in the training set and/or training parameters can lead to large changes in their generalization performance. Recent… (More)
Genetic algorithms, DNA computing, and in vitro evolution are brieey discussed. Elements of these are combined into laboratory procedures, and preliminary results are shown. The traditional test problem for genetic algorithms called the MAX 1s problem is addressed.
The multi-niche crowding genetic algorithm (MNC GA) has demonstrated its ability to maintain population diversity and stable subpopulations while allowing different species to evolve naturally in different niches of the fitness landscape. These properties are a consequence, in part, to the effect of crowding selection and worst among most similar… (More)
The determination of the sequence of all nucleotide base-pairs in a DNA molecule, from restriction-fragment data, is a complex task and can be posed as the problem of finding the optima of a multi-modal function. A genetic algorithm that uses multi-niche crowding permits us to do this. Performance of this algorithm is first tested using a standard suite of… (More)
Elements of evolutionary computation and molecular biology are combined to design a DNA evolutionary computation. The traditional test problem for evolutionary computation , OneMax problem is addressed. The key feature is the physical separation of DNA strands consistent with OneMax \\tness."
The development of quantitative structure-activity relationship (QSAR) models for computer-assisted drug design is a well-known technique in the pharmaceutical industry. QSAR models provide medicinal chemists with mechanisms for predicting the biological activity of compounds using their chemical structure or properties. This information can significantly… (More)
We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous… (More)
Aspects of Evolutionary Computation, DNA computing, and in vitro evolution are combined in proposed laboratory procedures. Preliminary experimental results are shown. The traditional test problem for Evolutionary Computation known as the OneMax problem is addressed. The preliminary experimental results indicate successful laboratory separation by tness" of… (More)