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Towards billion bit optimization via parallel estimation of distribution algorithm
 Genetic and Evolutionary Computation Conference (GECCO2007
, 2007
"... This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm (cGA) to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instan ..."
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Cited by 18 (9 self)
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This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm (cGA) to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of cGA. The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling up to a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The compact GA, on the other hand, is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billionvariable problems across the landscape of search problems.
Towards billion bit optimization via efficient genetic algorithms
, 2007
"... This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instances ov ..."
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Cited by 4 (2 self)
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This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of compact genetic algorithm (cGA). The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling up to a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The compact GA, on the other hand, is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billionvariable problems across the landscape of search problems. 1
Let’s Get Ready to Rumble Redux: Crossover Versus Mutation Head to Head on Exponentially Scaled Problems
"... This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of nonuniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks ( ..."
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Cited by 4 (1 self)
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This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of nonuniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentiallyscaled additively separable, problems, the BBwise mutation is more efficient than crossover yielding a speedup of o(ℓ log ℓ), where ℓ is the problem size. For the noisy exponentiallyscaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of o(ℓ log ℓ). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of o(ℓ).
Fluctuating Crosstalk, Deterministic Noise, and GA
"... This paper extends previous work showing how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. In that work, we modeled fluctuating crosstalk or nonlinear interactions among building blocks via higherorder Walsh coefficients. The fluctuating crossta ..."
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This paper extends previous work showing how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. In that work, we modeled fluctuating crosstalk or nonlinear interactions among building blocks via higherorder Walsh coefficients. The fluctuating crosstalk behaved like exogenous noise and could be handled by increasing the population size and run duration. This behavior held until the strength of the crosstalk far exceeded the underlying fitness variance by a certain factor empirically observed. This paper extends that work by considering fluctuating crosstalk effects on genetic algorithm scalability using smallerordered Walsh coefficients on two extremes of building block scaling: uniformlyscaled and exponentiallyscaled building blocks. Uniformlyscaled building blocks prove to be more sensitive to fluctuating crosstalk than do exponentiallyscaled building blocks in terms of function evaluations and run duration but less sensitive to population sizing for large buildingblock interactions. Our results also have implications for the relative performance of buildingblockwise mutation over crossover. Categories and Subject Descriptors
Fluctuating Crosstalk, GA Scalability, . . .
, 2007
"... The genetic algorithm (GA) is gaining increasing interest in both academia and industry in attempts to solve hard search problems quickly, accurately, and reliably. Various theories of what makes a problem difficult for the GA to solve have been put forward; yet, none of them has been completely con ..."
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The genetic algorithm (GA) is gaining increasing interest in both academia and industry in attempts to solve hard search problems quickly, accurately, and reliably. Various theories of what makes a problem difficult for the GA to solve have been put forward; yet, none of them has been completely confirmed experimentally. This thesis examines one theory of GA problem difficulty and investigates one facet of that theory that has received scant empirical attention and only passing theoretical consideration. The theory of problem difficulty assumed in this thesis is centered on the notion that GAs process building blocks in their search for optimal solutions. A source of difficulty commonly referred to in the literature is crosstalk, or nonlinear interactions among building blocks. The purpose of this thesis is to explore the effects of one type of crosstalk, fluctuating crosstalk, on population size, convergence time, and the number of function evaluations for boundedly difficult test functions. By modeling fluctuating crosstalk with Walsh coefficients, this thesis investigates fluctuating crosstalk effects on GA scalability by varying the order and magnitude of the crosstalk and the scaling of building blocks in the underlying fitness function. Highorder fluctuating
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 (CuxCo1−x) alloy show that GP predicts all barriers with 0.1 % error from calculations for less than 3 % of active