Richard K. Belew

Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individuals phenotype are reverse transcribed into its genotype and become heritable traits (sic). We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein–ligand test systems having known three‐dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein–ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure‐derived molecular properties was performed. The final model had a residual standard error of 9.11 kJ mol−1 (2.177 kcal mol−1) and was chosen as the new energy function. The new search methods and empirical free energy function are available in AUTODOCK, version 3.0. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1639–1662, 1998

AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand‐protein complexes and a cross‐docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid‐based docking method and a modification of the flexible sidechain technique.

New methods for competitive coevolution

We consider competitive coevolution, in which fitness is based on direct competition among individuals selected from two independently evolving populations of hosts and parasites. Competitive coevolution can lead to an arms race, in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. Competitive fitness sharing changes the way fitness is measured; shared sampling provides a method for selecting a strong, diverse set of parasites; and the hall of fame encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, and drift.

Automatic combination of multiple ranked retrieval systems

Retrieval performance can often be improved significantly by using a number of different retrieval algorithms and combining the results, in contrast to using just a single retrieval algorithm. This is because different retrieval algorithms, or retrieval experts, often emphasize different document and query features when determining relevance and therefore retrieve different sets of documents. However, it is unclear how the different experts are to be combined, in general, to yield a superior overall estimate. We propose a method by which the relevance estimates made by different experts can be automatically combined to result in superior retrieval performance. We apply the method to two expert combination tasks. The applications demonstrate that the method can identify high performance combinations of experts and also is a novel means for determining the combined effectiveness of experts.

Dynamic Parameter Encoding for Genetic Algorithms

The common use of static binary place-value codes for real-valued parameters of the phenotype in Hollands genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models.

Adaptive Retrieval Agents: Internalizing Local Contextand Scaling up to the Web

This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information environments on-line in search of pages relevant to the user, by traversing hyperlinks in an autonomous and intelligent fashion. Each agent adapts to the spatial and temporal regularities of its local context thanks to a combination of machine learning techniques inspired by ecological models: evolutionary adaptation with local selection, reinforcement learning and selective query expansion by internalization of environmental signals, and optional relevance feedback. We evaluate the feasibility and performance of these methods in three domains: a general class of artificial graph environments, a controlled subset of the Web, and (preliminarly) the full Web. Our results suggest that InfoSpiders could take advantage of the starting points provided by search engines, based on global word statistics, and then use linkage topology to guide their search on-line. We show how this approach can complement the current state of the art, especially with respect to the scalability challenge.

Adaptive information retrieval: using a connectionist representation to retrieve and learn about documents

AIR represents a connectionist approach to the task of information retrieval. The system uses relevance feedback from its users to change its representation of authors, index terms and documents so that, over time, AIR improves at its task. The result is a representation of the consensual meaning of keywords and documents shared by some group of users. The central focus goal of this paper is to use our experience with AIR to highlight those characteristics of connectionist representations that make them particularly appropriate for IR applications. We argue that this associative representation is a natural generalization of traditional IR techniques, and that connectionist learning techniques are effective in this setting.

Adaptive individuals in evolving populations: models and algorithms

* Introduction R.K. Belew and M. Mitchell Biology * Overview * Adaptive Computation in Ecology and Evolution: A Guide to Future Research J. Roughgarden, A. Bergman, S. Shafir, and C. Taylor Reprinted Classics * The Classics in Their Context, and in Ours J. Schull * Of the Influence of the Environment on the Activities and Habits of Animals, and the Influence of the Activities and Habits of These Living Bodies in Modifying Their Organization and Structure J.B. Lamarck * A New Factor in Evolution J.M. Baldwin * On Modification and Variation C. Lloyd Morgan * Canalization of Development and the Inheritance of Acquired Characters C.H. Waddington * The Baldwin Effect G.G. Simpson * The Role of Somatic Change in Evolution G. Bateson New Work * A Model of Individual Adaptive Behavior in a Fluctuating Environment L. A. Zhivotovsky, A. Bergman, and M. W. Feldman * The Baldwin Effect in the Immune System: Learning by Somatic Hypermutation R. Hightower, S. Forrest, and A. S. Perelson * The Effect of Memory Length on Individual Fitness in a Lizard S. Shafir and J. Roughgarden * Latent Energy Environments F. Menczer and R. K. Belew Psychology * Overview * The Causes and Effects of Evolutionary Simulation in the Behavioral Sciences P.M. Todd Reprinted Classics * Excerpts from Principles of Biology H. Spencer * Excerpts from Principles of Psychology H. Spencer * William James and the Broader Implications of a Multilevel Selectionism J. Schull * Excerpts from The Phylogeny and Ontogeny of Behavior B.F. Skinner * Excerpts from Adaptation and Intelligence: Organic Selection and Phenocopy J. Piaget * Selective Costs and Benefits of in the Evolution of Learning T. D. Johnston New Work * Sexual Selection and the Evolution of Learning P. M. Todd * Discontinuity in Evolution: How Different Levels of Organization Imply Preadaptation O. Miglino, S. Nolfi, and D. Parisi * The Influence of Learning on Evolution D. Parisi and S. Nolfi Computer Science * Overview * Computation and the Natural Sciences R. K. Belew, M. Mitchell, and D. H. Ackley Reprinted Classics * How Learning Can Guide Evolution G. E. Hinton and S. J. Nowlan * Natural Selection: When Learning Guides Evolution J. Maynard Smith New Work * Simulations Combining Evolution and Learning M. L. Littman * Optimization with Genetic Algorithm Hybrids that Use Local Searches W. E. Hart and R. K. Belew

Latent semantic indexing is an optimal special case of multidimensional scaling

Latent Semantic Indexing (LSI) is a technique for representing documents, queries, and terms as vectors in a multidimensional real-valued space. The representtions are approximations to the original term space encoding, and are found using the matrix technique of Singular Value Decomposition. In comparison Multidimensional Scaling (MDS) is a class of data analysis techniques for representing data points as points in a multidimensional real-valued space. The objects are represented so that inter-point similarities in the space match inter-object similarity information provided by the researcher. We illustrate how the document representations given by LSI are equivalent to the optimal representations found when solving a particular MDS problem in which the given inter-object similarity information is provided by the inner product similarities between the documents themselves. We further analyze a more general MDS problem in which the interdocument similarity information, although still in inner product form is arbitrary with respect to the vector space encoding of the documents.

Adaptive information agents in distributed textual environments

Hypertext cnvironmcnts such as the Web are rich with both word and link cues that can be exploited by autonomous ngents performing distributed tasks on behalf of the user. This paper characterizes such environments and identifies the fcaturcs that are most useful and readily available. We dcacribe the adaptive representation of an ecology of retrieval agents who attempt to capture important features of their surroundings, and base their behaviors upon them. We PERCUSS how such a representation allows the agents to interact with the environments where they are situated. Agents cnn internalize words that are locally correlated with fitness, based on user feedback. They are shown to outperform nonndaptivn search by an order of magnitude. Furthermore, cnch agent learns new strategies at local time and space scnlcs, while the population evolves at a global scale.