7221846
Evolving Sensor Suites for Enemy Radar Detection
Designing optimal teams of sensors to detect the enemy radars for military operations is a challenging design problem. Many applications require the need to manage sensor resources. There is a tradeoff between the need to decrease the cost and to increase the capabilities of a sensor suite. In this paper, we address this design problem using genetic algorithms. We attempt to evolve the characteristics, size, and arrangement of a team of sensors, focusing on minimizing the size of sensor suite while maximizing its detection capabilities. The genetic algorithm we have developed has produced promising results for different environmental configurations as well as varying sensor resources.
10203274
Adaptation of Length in a Nonstationary Environment
In this paper, we examine the behavior of a variable length GA in a nonstationary problem environment. Results indicate that a variable length GA is better able to adapt to changes than a fixed length GA. Closer examination of the evolutionary dynamics reveals that a variable length GA can in fact take advantage of its variable length representation to exploit good quality building blocks after a change in the problem environment.
2884857
A genetic approach to planning in heterogeneous computing environments
Planning is an artificial intelligence problem with a wide range of real-world applications. Genetic algorithms, neural networks, and simulated annealing are heuristic search methods often used to solve complex optimization problems. In this paper, we propose a genetic approach to planning in the context of workflow management and process coordination on a heterogenous grid. We report results for two planning problems, the Towers of Hanoi and the Sliding-tile puzzle.
18463797
Planning with Recursive Subgoals
In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a genetic-based planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a goal into a set of serializable subgoals and to specify a strict ordering among the subgoals. Empirical results show that the recursive subgoal strategy reduces the size of the search space and improves the quality of solutions to planning problems.
15665141
An incremental genetic algorithm approach to multiprocessor scheduling
We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.
39372956
An Inequality on Connected Domination Parameters
11499161
Minimal replication cost for availability
Today, the utility of many replicated Internet services is limited by availability rather than raw performance. To better understand the effects of replica placement on availability, we propose the problem of minimal replication cost for availability. Let replication cost be the cost associated with replica deployment, dynamic replica creation and teardown at n candidate locations. Given client access patterns, replica failure patterns, network partition patterns, a required consistency level and a target level of availability, the minimal replication cost is the lower bound on a system's replication cost. Solving this problem also answers the dual question of optimal availability given a constraint on replication cost.We design the first algorithm we are aware of to solve the problem, through reduction to integer linear programming and enumeration of pruned serialization orders. Using practical faultloads and workloads, we demonstrate that the exponential complexity of our algorithm is tractable for practical problems with hundreds of candidate locations. The lower bound computed by our algorithm is tight, but the tightness can be sacrificed by a proposed optimization for large problems. We also show that with low replica creation and teardown costs, the bound is close to tight in practical problems even with the optimization.
14427569
Design and evaluation of a continuous consistency model for replicated services
The tradeoffs between consistency, performance, and availability are well understood. Traditionally, however, designers of replicated systems have been forced to choose from either strong consistency guarantees or none at all. This paper explores the semantic space between traditional strong and optimistic consistency models for replicated services. We argue that an important class of applications can tolerate relaxed consistency, but benefit from bounding the maximum rate of inconsistent access in an application-specific manner. Thus, we develop a set of metrics, Numerical Error, Order Error, and Staleness, to capture the consistency spectrum. We then present the design and implementation of TACT, a middleware layer that enforces arbitrary consistency bounds among replicas using these metrics. Finally, we show that three replicated applications demonstrate significant semantic and performance benefits from using our framework.
30353816
SybilGuard: defending against sybil attacks via social networks
5731604
A flexible method to tolerate value sensor failures
Tolerating the value failures of sensors is an important problem in automated control processes and plants. In this paper, we address this problem in a theoretical framework in order to demonstrate the feasibility of an automatic method based on discrete controller synthesis. We consider a fault-intolerant program whose job is to control an automated process, here a liquid tank equipped with level sensors that can be subject to value faults. This fault-intolerant program is modeled as a finite labeled transition system. We then specify formally a fault hypothesis, i.e., how many sensors can fail simultaneously. We use discrete controller synthesis to obtain automatically a program, having the same behavior as the initial fault-intolerant one, and satisfying the fault tolerance requirements under the fault hypothesis. We advocate that, thanks to the use of discrete controller synthesis, our method offers flexibility, reliability, separation of concern, and it is automatic.