A genomic computing network is a variant of a neural network for which a genome encodes all aspects, both structural and functional, of the network. The genome is evolved by a genetic algorithm to fit particular tasks and environments. The genome has three portions: one for specifying links and their initial weights, a second for specifying how a node updates its internal state, and a third for specifying how a node updates the weights on its links. Preliminary experiments demonstrate that genomic computing networks can use node internal state to solve POMDPs more complex than those solved previously using neural networks.

Mobile ad hoc networks are typically designed and evaluated in generic simulation environments. However the real conditions in which these networks are deployed can be quite different in terms of RF attentution, topology, and traffic load. Furthermore, specific situations often have a need for a network that is optimized along certain characteristics such as delay, energy or overhead. In response to the variety of conditions and requirements, ad hoc networking protocols are often designed with many modifiable parameters. However, there is currently no methodical way for choosing values for the parameters other than intuition and broad experience. In this paper we investigate the use of genetic algorithms for automated selection of parameters in an ad hoc networking system. We provide experimental results demonstrating that the genetic algorithm can optimize for different classes of operating conditions. We also compare our genetic algorithm optimization against hand-tuning in a complex, realistic scenario and show how the genetic algorithm provides better performance.

We investigate the problem of scheduling the move of a large amount of military equipment from a fort or depot to a port. This problem differs from traditional distribution scheduling problems in a number of ways including: (i) the trucks need to be grouped into convoys, (ii) there is a single source location and a single destination, and (iii) there are potentially so many trucks traveling the same set of roads that the effects on other traffic must be considered. We have divided the problem into two parts: (i) selecting a fixed set of routes and (ii) forming the trucks into convoys and selecting routes and departure times for each convoy. We describe how we have used genetic algorithms to solve each of these problems. We emphasize how the ability to incorporate domain knowledge into the genetic algorithms has allowed us to easily create algorithms well suited to the particular constraints of the problems.

Applications that require real-time scheduling of large-scale problems in complex domains present a number of difficulties for search and optimization techniques. These difficulties include: (i) a search space whose size grows exponentially with the size of the problem, (ii) a problem that is constantly changing due to a changing environment and user interaction, and (iii) the need to trade off between a variety of different criteria measuring the relative fitness of a particular schedule. BBN has used genetic algorithms to solve a variety of real-world scheduling problems, including applications in areas such as field service scheduling, job shop scheduling, transportation scheduling, and laboratory experiment scheduling. With the recent acquisition of BBN by GTE, we expect that our scheduling technology will soon be deployed to solve hard scheduling problems in network operations management. Our genetic algorithm technology addresses the issues above and provides a domain-independent infrastructure upon which we have rapidly developed customized software solutions for various customers. Because the infrastructure is domain independent, adding network operations management to the list of domains in which our scheduling technology has been successfully applied should be straightforward.

Real-time scheduling of large-scale problems in complex domains presents a number of difficulties for search and optimization techniques, including: large and complex search spaces; dynamically changing problems; and a variety of problem-dependent constraints and preferences. Genetic algorithms are well suited to such problems due to their adaptability and their effectiveness at searching large spaces. We have used genetic algorithms to solve real-world problems in areas such as field service scheduling, air crew scheduling and transportation scheduling. We discuss key aspects of our approach including: domain-specific chromosome representation and genetic operators; multi-objective evaluation function; heuristic initialization of the population; dynamic rescheduling; and cooperative interaction with human operators.

We have developed a completely automated approach to hardware design based on integrating three core technologies into one comprehensive system, namely genetic programming (GP), the VHSIC Hardware Description Language (VHDL) and field programmable gate arrays (FPGAs). Our system uses an automated GP engine, as opposed to a human designer, to evolve a hardware design composed of one or more FPGAs that will maximally achieve an application's software requirements. Several variants of our system exist; other variants are currently under development. The focus of this paper is to describe our original system design and its most recent revision to date.

Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and/or unnecessarily poor generalization performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix/vector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.

In this paper, we derive a configuration-space description of the kinematics of the fingers-plus-object system. To do this, we first formulate contact kinematics as a "virtual" kinematic chain. Then, the system can be viewed as one large closed kinematic chain composed of smaller chains, one for each finger and one for each contact point. We examine the underlying configuration space and two ways of moving through this space. The first, kinematics-based velocity control, is a generalization of some previous velocity-based approaches. The second, hyperspace jumps, is a purely configuration-space concept. We conclude with a discussion of how these concepts can be used to understand the task of twirling a baton.>

The DARPA SIMNET project allows hundreds of soldiers to train together in a virtual air, land, and sea environment through a network of interactive simulators. In addition to the manned simulators, the virtual environment is also populated by a large number of autonomous vehicles called semi-automated forces, which are controlled by an operator at a single workstation. The authors address the issues of collision avoidance and formation keeping. The autonomous vehicles are responsible for the lower-level path planning, collision avoidance, and formation following. Routines are described for maneuvering among large obstacles, smaller objects, and moving vehicles.>

Two types of grasp stability, spatial grasp stability and contact grasp stability, each with a different concept of the state of a grasp, are distinguished and characterized. Examples are presented to show that spatial stability cannot capture certain intuitive concepts of grasp stability and hence that any full understanding of grasp stability must include contact stability. A model of how the positions of the points of contact evolve in time on the surface of a grasped object in the absence of any external force or active feedback is then derived. From the model, a general measure of the contact stability of any two-fingered grasp is obtained. Finally, the consequences of this stability measure and a related measure of contact manipulability on strategies for grasp selection are discussed.>

The author distinguishes between two types of grasp stability, called spatial grasp stability and contact grasp stability. The former is the tendency of the grasped object to return to an equilibrium location in space; the latter is the tendency of the points of contact to return to an equilibrium position on the object's surface. It is shown, via examples, that spatial stability cannot capture certain intuitive concepts of grasp stability, and hence that any full understanding of grasp stability must include contact stability. A model of how the positions of the points of contact evolve in time on the surface of the grasped object in the absence of any external force or active feedback is derived. From this model, a condition is obtained which determines whether or not a two-fingered grasp is contact stable.>

The kinematics of contact describes the motion of a point of contact over the surfaces of two contacting objects in response to a relative motion of these objects. In a previous work (Int. of Robotics Res., vol.3, p.17-32, 1988), the author derived equations that embody this relationship when the two objects are assumed to be rigid bodies. In the present work, he extends that analysis by dropping the assumption of rigidity. He derives a set of equations, called the compliant contact equations, which model the kinematics of contact with compliance. He discusses an example that illustrates the effect of compliance on the kinematics of contact. He measures the trajectory of the center of contact on a tactile sensor in response to a known motion and shows how the results fit the proposed model. He analyzes how two tasks that are based on the rigid-body model of the kinematics of contact have been designed to be robust with respect to compliance. For the task of contour-following he provides experimental results that confirm this analysis.>