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17
Impact of network density on Data Aggregation in wireless sensor networks
, 2001
"... Innetwork data aggregation is essential for wireless sensor networks where resources (e.g., bandwidth, energy) are limited. In a previously proposed data dissemination scheme, data is opportunistically aggregated at the intermediate nodes on a lowlatency tree which may not necessarily be energy ef ..."
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Cited by 307 (5 self)
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Innetwork data aggregation is essential for wireless sensor networks where resources (e.g., bandwidth, energy) are limited. In a previously proposed data dissemination scheme, data is opportunistically aggregated at the intermediate nodes on a lowlatency tree which may not necessarily be energy efficient. A more energyefficient tree is a greedy tree which can be incrementally constructed by connecting each source to the closest point of the existing tree. In this paper, we propose a greedy approach for constructing a greedy aggregation tree to improve path sharing. We evaluated the performance of this greedy approach by comparing it to the prior opportunistic approach. Our preliminary result suggests that although the greedy aggregation and the opportunistic aggregation are roughly equivalent at lowdensity networks, the greedy aggregation can achieve signficant energy savings at higher densities. In one experiment we found that the greedy aggregation can achieve up to 45 % energy savings over the opportunistic aggregation without an adverse impact on latency or robustness.
A Comprehensive Survey of EvolutionaryBased Multiobjective Optimization Techniques
 Knowledge and Information Systems
, 1998
"... . This paper presents a critical review of the most important evolutionarybased multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search cap ..."
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Cited by 265 (22 self)
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. This paper presents a critical review of the most important evolutionarybased multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms. Each technique is briefly described mentioning its advantages and disadvantages, their degree of applicability and some of their known applications. Finally, the future trends in this discipline and some of the open areas of research are also addressed. Keywords: multiobjective optimization, multicriteria optimization, vector optimization, genetic algorithms, evolutionary algorithms, artificial intelligence. 1 Introduction Since the pioneer work of Rosenberg in the late 60s regarding the possibility of using geneticbased search to deal with multiple objectives, this new area of research (now called evolutionary multiobjective optimization) has grown c...
Adaptive Penalty Methods For Genetic Optimization Of Constrained Combinatorial Problems
 INFORMS Journal on Computing
, 1996
"... The application of genetic algorithms (GA) to constrained optimization problems has been hindered by the inefficiencies of reproduction and mutation when feasibility of generated solutions is impossible to guarantee and feasible solutions are very difficult to find. Although several authors have ..."
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Cited by 36 (15 self)
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The application of genetic algorithms (GA) to constrained optimization problems has been hindered by the inefficiencies of reproduction and mutation when feasibility of generated solutions is impossible to guarantee and feasible solutions are very difficult to find. Although several authors have suggested the use of both static and dynamic penalty functions for genetic search, this paper presents a general adaptive penalty technique which makes use of feedback obtained during the search along with a dynamic distance metric. The effectiveness of this method is illustrated on two diverse combinatorial applications; (1) the unequalarea, shapeconstrained facility layout problem and (2) the seriesparallel redundancy allocation problem to maximize system reliability given cost and weight constraints. The adaptive penalty function is shown to be robust with regard to random number seed, parameter settings, number and degree of constraints, and problem instance. 1. Introduction ...
Multicriteria Decision Making and Evolutionary Computation
, 1996
"... Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the ..."
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Cited by 14 (0 self)
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Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the COMBINED problem of searching large spaces to meet multiple objectives. While multicriteria decision analysis usually assumes a small number of alternative solutions to choose from, or an "easy" (e.g., linear) space to search, research on robust search methods generally assumes some way of aggregating multiple objectives into a single figure of merit. This traditional separation of search and multicriteria decisions allows for two straightforward hybrid strategies: (1) make multicriteria decisions FIRST, to aggregate objectives, then apply EC search to optimize the resulting figure of merit, or (2) conduct multiple EC searches FIRST using different aggregations of the objectives in order to o...
Genetic Algorithm Approach To The Search For Golomb Rulers
 6th International Conference on Genetic Algorithms (ICGA’95
, 1995
"... The success of genetic algorithm in finding relatively good solutions to NPcomplete problems such as the traveling salesman problem and jobshop scheduling problem provided a good starting point for a machine intelligent method of finding Golomb Rulers. These rulers have been applied to radio astro ..."
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Cited by 12 (1 self)
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The success of genetic algorithm in finding relatively good solutions to NPcomplete problems such as the traveling salesman problem and jobshop scheduling problem provided a good starting point for a machine intelligent method of finding Golomb Rulers. These rulers have been applied to radio astronomy, Xray crystallography, circuit layout and geographical mapping. Currently the shortest lengths of the first sixteen rulers are known. The nature of NPcomplete makes the search for higher order rulers difficult and very time consuming. While the shortest lengths for each order are important as a mathematical exercise, finding relatively short high order valid rulers has a more important impact on real world applications.
A Comparative Study of a Penalty Function, a Repair Heuristic, and Stochastic Operators with the SetCovering Problem
"... In this paper we compare the effects of using various stochastic operators with the nonunicost setcovering problem. Four different crossover operators are compared to a repair heuristic which consists in transforming infeasible strings into feasible ones. These stochastic operators are incorporated ..."
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Cited by 11 (0 self)
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In this paper we compare the effects of using various stochastic operators with the nonunicost setcovering problem. Four different crossover operators are compared to a repair heuristic which consists in transforming infeasible strings into feasible ones. These stochastic operators are incorporated in GENEsYs [2], the genetic algorithm we apply to problem instances of the setcovering problem we draw from well known test problems. GENEsYs uses a simple fitness function that has a graded penalty term to penalize infeasibly bred strings. The results are compared to a non GAbased algorithm based on the greedy technique. Our computational results are then compared, shedding some light on the effects of using different operators, a penalty function, and a repair heuristic on a highly constrained combinatorial optimization problem.
Using PVM for Hunting SnakeintheBox codes
 PROCEEDINGS OF THE 1994 TRANSPUTER RESEARCH AND APPLICATIONS CONFERENCE
, 1994
"... An Incoil in Qn, the ndimensional unit cube, is a simple cycle C in Qn such that C has no chords in Qn. An Insnake is a simple (open) path S in Qn which has no chords in Qn. The problem of finding a snake of maximum length suffers from severe combinatorial explosion. This paper describes the use ..."
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Cited by 3 (0 self)
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An Incoil in Qn, the ndimensional unit cube, is a simple cycle C in Qn such that C has no chords in Qn. An Insnake is a simple (open) path S in Qn which has no chords in Qn. The problem of finding a snake of maximum length suffers from severe combinatorial explosion. This paper describes the use of a Genetic Algorithm for finding snakes, and how thiscodewas interfaced with the software package PVM (Parallel Virtual Machine), allowing us to adapt GA code written for single processor machines for use on a cluster of single processor machines acting in parallel.
A Genetic Algorithm Model for Mission Planning and Dynamic Resource Allocation of Airborne Sensors
, 1999
"... Genetic Algorithms (GA) have been very successful in combinatoriallyexplosive problems such as; Job Shop Scheduling, Nonlinear Transportation Model, and the Traveling Salesman Problem. The problem of mission planning for large numbers of airborne platforms contains elements of all three of these ..."
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Cited by 2 (0 self)
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Genetic Algorithms (GA) have been very successful in combinatoriallyexplosive problems such as; Job Shop Scheduling, Nonlinear Transportation Model, and the Traveling Salesman Problem. The problem of mission planning for large numbers of airborne platforms contains elements of all three of these classic GA problems. This paper will outline the application of genetic algorithms to mission planning and dynamic allocation of airborne sensors. An experiment will consist of a mission simulation containing (n) airborne sensors in different locations and states of readiness, and (m) requests for imagery with varying mission priorities. The GA will match sensors with requests in order to minimize the cost and timeline and maximize the execution of high priority requests. Results of simulation will be discussed. 1
Geneticalgorithmbased design of groundwater quality monitoring system
, 1993
"... Department of the Interior as authorized under the Water Resources Research Act of 1984. The contents of this report do not necessarily reflect the views and policies of the U.S. Department of the Interior, nor does mention of trade names or commercial products constitute their endorsement by the Un ..."
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Cited by 2 (0 self)
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Department of the Interior as authorized under the Water Resources Research Act of 1984. The contents of this report do not necessarily reflect the views and policies of the U.S. Department of the Interior, nor does mention of trade names or commercial products constitute their endorsement by the United States Government. The University of Illinois is an equal opportunityJaffmative action institution.