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A Matrix Approach for Finding Extrema: PROBLEMS WITH MODULARITY, HIERARCHY, AND OVERLAP
, 2006
"... Unlike most simple textbook examples, the real world is full with complex systems, and researchers in many different fields are often confronted by problems arising from such systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve lar ..."
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Cited by 9 (0 self)
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Unlike most simple textbook examples, the real world is full with complex systems, and researchers in many different fields are often confronted by problems arising from such systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition according to the complex system is the key. In this research project, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three barebone types of interactionsâ€”modularity, hierarchy, and overlap, theories and models are developed to dissect and inspect problem decomposition in the context of genetic algorithms. This dissertation presents a research project to develop a competent optimization method to solve boundedly difficult problems with modularity, hierarchy, and overlap by explicit problem decomposition. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and decompose the problem. The results from this thesis should benefit research both technically and scientifically. Technically, this thesis develops an automated dependency structure matrix clustering technique and utilizes it to design a competent blackbox problem solver. Scientifically, the explicit interaction
Missouri Estimation of Distribution Algorithms
"... This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techni ..."
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This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
Finding Ground States of . . . with Hierarchical BOA and Genetic Algorithms
, 2008
"... This study focuses on the problem of finding ground states of random instances of the SherringtonKirkpatrick (SK) spinglass model with Gaussian couplings. While the ground states of SK spinglass instances can be obtained with branch and bound, the computational complexity of branch and bound yiel ..."
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This study focuses on the problem of finding ground states of random instances of the SherringtonKirkpatrick (SK) spinglass model with Gaussian couplings. While the ground states of SK spinglass instances can be obtained with branch and bound, the computational complexity of branch and bound yields instances of not more than about 90 spins. We describe several approaches based on the hierarchical Bayesian optimization algorithm (hBOA) to reliably identifying ground states of SK instances intractable with branch and bound, and present a broad range of empirical results on such problem instances. We argue that the proposed methodology holds a big promise for reliably solving large SK spinglass instances to optimality with practical time complexity. The proposed approaches to identifying global optima reliably can also be applied to other problems and they can be used with many other evolutionary algorithms. Performance of hBOA is compared to that of the genetic algorithm with two common crossover operators.
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, 710
"... Effective linkage learning using loworder statistics and clustering 1 ..."
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, 710
"... Effective linkage learning using loworder statistics and clustering 1 ..."
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Genetic Algorithms and . . . MODELING: APPLICATIONS IN MATERIALS SCIENCE AND CHEMISTRY AND ADVANCES IN SCALABILITY
, 2007
"... Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for mult ..."
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Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two nontrivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multitimescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span ordersofmagnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a nontrivial example of vacancyassisted migration on a surface of a facecentered cubic (fcc) CopperCobalt (CuxCo1âˆ’x) alloy show that GP predicts all barriers with 0.1 % error from calculations for less than 3 % of active