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16
Bayesian Optimization Algorithm: From Single Level to Hierarchy
, 2002
"... There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decompositi ..."
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Cited by 101 (19 self)
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There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decomposition as opposed to decomposition on only a single level. Third, design a class of difficult hierarchical problems that can be used to test the algorithms that attempt to exploit hierarchical decomposition. Fourth, test the developed algorithms on the designed class of problems and several realworld applications. The dissertation proposes the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model the promising solutions found so far and sample new candidate solutions. BOA is theoretically and empirically shown to be capable of both learning a proper decomposition of the problem and exploiting the learned decomposition to ensure robust and scalable search for the optimum across a wide range of problems. The dissertation then identifies important features that must be incorporated into the basic BOA to solve problems that are not decomposable on a single level, but that can still be solved by decomposition over multiple levels of difficulty. Hierarchical
A review of adaptive population sizing schemes in genetic algorithms
 In: Proc. GECCO’05
, 2005
"... This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various selfadjusting population sizing schemes that have been proposed in the literature. The pap ..."
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Cited by 28 (4 self)
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This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various selfadjusting population sizing schemes that have been proposed in the literature. The paper ends with recommendations for those who design and compare adaptive population sizing schemes for genetic algorithms.
Efficient genetic algorithms using discretization scheduling
, 2002
"... In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations are relaxed from using numerical methods such as numerical integration. In these types of applications, the cost and accuracy vary from discretization errors when implic ..."
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Cited by 10 (1 self)
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In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations are relaxed from using numerical methods such as numerical integration. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. There may be several functions with different grid sizing to obtain a given solution quality. This thesis examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. Time budgeting is used to estimate the computational resources needed, and there are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one and twodimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling.
Efficient Discretization Scheduling in Multiple Dimensions
 PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY CONFERENCE 2002
, 2002
"... There is a tradeoff between speed and accuracy in fitness evaluations when various discretization sizes are used to estimate the fitness. This paper introduces discretization scheduling, which varies the size of the discretization within the GA, and compares this method to using a constant discre ..."
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Cited by 4 (0 self)
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There is a tradeoff between speed and accuracy in fitness evaluations when various discretization sizes are used to estimate the fitness. This paper introduces discretization scheduling, which varies the size of the discretization within the GA, and compares this method to using a constant discretization. It will be shown that when scheduling the discretization, less computation time is used without sacrificing solution quality. Fitness functions whose cost and accuracy vary because of discretization errors from numerical integration are considered, and the speedup achieved from using efficient discretizations is predicted and shown empirically.
Scalability of genetic programming and probabilistic incremental program evolution
 Genetic and Evolutionary Computation Conference (GECCO 2005), 1785–1786. (Preprint: arXiv:cs.NE/0502029
, 2005
"... This paper discusses scalability of standard genetic programming (GP) and the probabilistic incremental program evolution (PIPE). To investigate the need for both effective mixing and linkage learning, two test problems are considered: ORDER problem, which is rather easy for any recombinationbased G ..."
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Cited by 4 (1 self)
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This paper discusses scalability of standard genetic programming (GP) and the probabilistic incremental program evolution (PIPE). To investigate the need for both effective mixing and linkage learning, two test problems are considered: ORDER problem, which is rather easy for any recombinationbased GP, and TRAP or the deceptive trap problem, which requires the algorithm to learn interactions among subsets of terminals. The scalability results show that both GP and PIPE scale up polynomially with problem size on the simple ORDER problem, but they both scale up exponentially on the deceptive problem. This indicates that while standard recombination is sufficient when no interactions need to be considered, for some problems linkage learning is necessary. These results are in agreement with the lessons learned in the domain of binarystring genetic algorithms (GAs). Furthermore, the paper investigates the effects of introducing unnecessary and irrelevant primitives on the performance of GP and PIPE.
On the design and analysis of competent selectorecombinative GAs
 Evolutionary Computation
"... In this paper, we study two recent theoretical models — a populationsizing model and a convergence model — and examine their assumptions to gain insights into the conditions under which selectorecombinative GAs work well. We use these insights to formulate several design rules to develop compete ..."
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Cited by 2 (1 self)
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In this paper, we study two recent theoretical models — a populationsizing model and a convergence model — and examine their assumptions to gain insights into the conditions under which selectorecombinative GAs work well. We use these insights to formulate several design rules to develop competent GAs for practical problems. To test the usefulness of the design rules, we consider as a case study the maplabeling problem, an NPhard problem from cartography. We compare the predictions of the theoretical models with the actual performance of the GA for the maplabeling problem. Experiments show that the predictions match the observed scaleup behavior of the GA, thereby strengthening our claim that the design rules can guide the design of competent selectorecombinative GAs for realistic problems.
Directed Intervention Crossover Approaches in Genetic Algorithms with Application to Optimal Control Problems
, 2009
"... Genetic Algorithms (GAs) are a search heuristic technique modelled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treat ..."
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Cited by 2 (1 self)
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Genetic Algorithms (GAs) are a search heuristic technique modelled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other search heuristics to find fit solutions. This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type. The directed intervention crossover techniques use intervention scheduling information from parent solutions to direct the offspring produced in the GA crossover process towards more promising areas of a search space. By counting the number of interventions present in parents and adjusting the number of interventions for offspring schedules around it, this allows for highly fit solutions to be found in less fitness function evaluations. The validity of these novel approaches is illustrated through comparison with conventional GA crossover approaches for optimisation of intervention schedules of biocontrol application in mushroom farming and cancer chemotherapy treatment. These involve optimally scheduling the application of a biocontrol agent to combat pests in mushroom farming and optimising the timing and dosage strength of cancer chemotherapy treatments to
On the design and analysis of competent GAs
, 2002
"... We study two recent theoretical modelsa populationsizing model and a convergence modeland examine their assumptions to gain insights about the conditions under which GAs work well. We use these insights to formulate several design rules to develop competent GAs for practical problems. We t ..."
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We study two recent theoretical modelsa populationsizing model and a convergence modeland examine their assumptions to gain insights about the conditions under which GAs work well. We use these insights to formulate several design rules to develop competent GAs for practical problems. We then use these rules to design a GA that solves the maplabeling problem, an NPhard problem of realworld significance. Finally, we test whether the fact that our GA followed the design rules inspired by the theoretical models results in a scaleup behavior as predicted by these models. Experiments show that this is indeed the case.
Aruna Rani / Indian Journal of Computer Science and Engineering (IJCSE) MSA AGENT FOR MULTIMEDIA APPLICATIONS
"... The MSA Agent is the software developed to design rectangular and U slotted micro strip antenna. It is applied for the various applications such as satellite communications, UHF applications. Within the multimedia frequency range the developed software is tested and analyzed for various results. The ..."
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The MSA Agent is the software developed to design rectangular and U slotted micro strip antenna. It is applied for the various applications such as satellite communications, UHF applications. Within the multimedia frequency range the developed software is tested and analyzed for various results. The software is developed using genetic algorithm. This provides extra flexibility and new capability to design rectangular and U slotted micro strip antenna for multimedia application also. The result shows good agreement with earlier reported result. Index terms Multimedia, frequency slotted micro strip antenna, MSA agent, genetic algorithm, Micro strip
Design and Development of MSA agent for Rectangular and U Slotted MSA
"... Recently, research interest has increased in the design, development, and deployment of microstrip antenna agent systems for highlevel inference and surveillance in a wireless sensor network (WSN). The proposed Antenna agent systems employ migrating codes to facilitate flexible application retaskin ..."
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Recently, research interest has increased in the design, development, and deployment of microstrip antenna agent systems for highlevel inference and surveillance in a wireless sensor network (WSN). The proposed Antenna agent systems employ migrating codes to facilitate flexible application retasking, local processing, and calculating parameters. This provides extra flexibility, as well as new capabilities to MSA. It employs the genetic Algorithm to generate various results. This provides extra flexibility as well as new capability to design Rectangular and U slotted Microstrip Antenna.The development of Rectangular & U slit loaded antenna into Algorithm design based on parameter analysis and the coding for the architecture of rectangular and U slot loaded MSA agent. The taxonomy covers low level to high level design issue and facilitate the variation in design parameters like h, fr, Ɛ r, flow, fhigh, R according to the requirement of the proposed agent. IndexTerms Microstrip antenna, communication, dielectric constant, frequency, bandwidth, java program,