34073205
Genetic Optimization Using A Penalty Function
24475
Designing resilient networks using a hybrid genetic algorithm approach
As high-speed networks have proliferated across the globe, their topologies have become sparser due to the increased capacity of communication media and cost considerations. Reliability has been a traditional goal within network design optimization of sparse networks. This paper proposes a genetic approach that uses network resilience as a design criterion in order to ensure the integrity of network services in the event of component failures. Network resilience measures have been previously overlooked as a network design objective in an optimization framework because of their computational complexity - requiring estimation by simulation. This paper analyzes the effect of noise in the simulation estimator used to evaluate network resilience on the performance of the proposed optimization approach.
5725147
Local search genetic algorithm for optimal design of reliable networks
This paper presents a genetic algorithm (GA) with specialized encoding, initialization, and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the fitness function involves calculating the all-terminal reliability of the network, which is a computationally expensive calculation. Therefore, it is imperative that the search balances the need to thoroughly explore the boundary between feasible and infeasible networks, along with calculating fitness on only the most promising candidate networks. The algorithm results are compared to optimum results found by branch and bound and also to GA results without local search operators on a suite of 79 test problems. This strategy of employing bounds, simple heuristic checks, and problem-specific repair and local search operators can be used on other highly constrained combinatorial applications where numerous fitness calculations are prohibitive.
6414828
Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks
This paper presents a genetic algorithm (GA) with specialized encoding, initialization and local search genetic operators to optimize communication network topologies. This NPhard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible network architectures. Compounding this infeasibility issue is that the fitness function involves calculating the all-terminal reliability of the network, a calculation which is computationally expensive. Therefore, it is imperative that the search balances the need to thoroughly explore the boundary between feasible and infeasible networks, along with calculating fitness on only the most promising candidate networks. The algorithm results are compared to optimum results found by branch and bound and also to GA results without local search operators on a suite of 79 test problems. This GA strategy of employing bounds, simple heuristic checks and problem specific repair and local search operators can be used on other highly constrained combinatorial applications where numerous fitness calculations are prohibitive.
14362355
Design diagnosis using Boolean satisfiability
Recent advances in Boolean satisfiability have made it an attractive engine for solving many digital VLSI design problems such as verification, model checking, optimization and test generation. Fault diagnosis and logic debugging have not been addressed by existing satisfiability-based solutions. This paper attempts to bridge this gap by proposing a satisfiability-based solution to these problems. The proposed formulation is intuitive and easy to implement. It shows that satisfiability captures significant problem characateristics and it offers different trade-offs. It also provides new opportunities for satisfiability-based diagnosis tools and diagnosis-specific satisfiability algorithms. Theory and experiments validate the claims and demonstrate its potential.
19034554
SIMPROT: Using an empirically determined indel distribution in simulations of protein evolution
BackgroundGeneral protein evolution models help determine the baseline expectations for the evolution of sequences, and they have been extensively useful in sequence analysis and for the computer simulation of artificial sequence data sets.ResultsWe have developed a new method of simulating protein sequence evolution, including insertion and deletion (indel) events in addition to amino-acid substitutions. The simulation generates both the simulated sequence family and a true sequence alignment that captures the evolutionary relationships between amino acids from different sequences. Our statistical model for indel evolution is based on the empirical indel distribution determined by Qian and Goldstein. We have parameterized this distribution so that it applies to sequences diverged by varying evolutionary times and generalized it to provide flexibility in simulation conditions. Our method uses a Monte-Carlo simulation strategy, and has been implemented in a C++ program named Simprot.ConclusionSimprot will be useful for testing methods of analysis of protein sequence families particularly alignment methods, phylogenetic tree building, detection of recombination and horizontal gene transfer, and homology detection, where knowing the true course of sequence evolution is essential.
13460881
Multi-objective tabu search using a multinomial probability mass function
A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible.
1725701
Cache performance for multimedia applications
The caching behavior of multimedia applications has been described as having high instruction reference locality within small loops, very large working sets, and poor data cache performance due to non-locality of data references. Despite this, there is no published research deriving or measuring these qualities. Utilizing the previously developed Berkeley Multimedia Workload, we present the results of execution driven cache simulations with the goal of aiding future media processing architecture design. Our analysis examines the differences between multimedia and traditional applications in cache behavior. We find that multimedia applications actually exhibit lower instruction miss ratios and comparable data miss ratios when contrasted with other widely studied workloads. In addition, we find that longer data cache line sizes than are currently used would benefit multimedia processing.
7100956
Scheduling techniques for reducing processor energy use in MacOS
The CPU is one of the major power consumers in a portable computer, and considerable power can be saved by turning off the CPU when it is not doing useful work. In Apple's MacOS, however, idle time is often converted to busy waiting, and generally it is very hard to tell when no useful computation is occurring. In this paper, we suggest several heuristic techniques for identifying this condition, and for temporarily putting the CPU in a low‐power state. These techniques include turning off the processor when all processes are blocked, turning off the processor when processes appear to be busy waiting, and extending real time process sleep periods. We use trace‐driven simulation, using processor run interval traces, to evaluate the potential energy savings and performance impact. We find that these techniques save considerable amounts of processor energy (as much as 66%), while having very little performance impact (less than 2% increase in run time). Implementing the proposed strategies should increase battery lifetime by approximately 20% relative to Apple's current CPU power management strategy, since the CPU and associated logic are responsible for about 32% of power use; similar techniques should be applicable to operating systems with similar behavior.