Slideshows for Tag: bayesian-optimization-algorithm http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows for Tag: bayesian-optimization-algorithm http://www.slideshare.net/ Tue, 25 Sep 2007 11:57:08 GMT SlideShare feed for Slideshows for Tag: bayesian-optimization-algorithm Fitness inheritance in the Bayesian optimization algorithm http://www.slideshare.net/pelikan/fitness-inheritance-in-the-bayesian-optimization-algorithm
This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more.]]>
This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more.]]> Tue, 25 Sep 2007 11:57:08 GMT http://www.slideshare.net/pelikan/fitness-inheritance-in-the-bayesian-optimization-algorithm pelikan@slideshare.net(pelikan) Fitness inheritance in the Bayesian optimization algorithm pelikan This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more. <img src="http://cdn.slidesharecdn.com/fitness-inheritance-in-the-bayesian-optimization-algorithm943-thumbnail-2?1190721428" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more. Fitness inheritance in the Bayesian optimization algorithm
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]]> 1118 0 http://cdn.slidesharecdn.com/fitness-inheritance-in-the-bayesian-optimization-algorithm943-thumbnail-2?1190721428 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Computational complexity and *simulation of rare events of Ising spin glasses http://www.slideshare.net/pelikan/computational-complexity-and-simulation-of-rare-events-of-ising-spin-glasses
We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms---such as the average number of evaluations until convergence---can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search.]]>
We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms---such as the average number of evaluations until convergence---can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search.]]> Tue, 25 Sep 2007 11:50:02 GMT http://www.slideshare.net/pelikan/computational-complexity-and-simulation-of-rare-events-of-ising-spin-glasses pelikan@slideshare.net(pelikan) Computational complexity and *simulation of rare events of Ising spin glasses pelikan We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms---such as the average number of evaluations until convergence---can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search. <img src="http://cdn.slidesharecdn.com/computational-complexity-and-simulation-of-rare-events-of-ising-spin-glasses4173-thumbnail-2?1190721002" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms---such as the average number of evaluations until convergence---can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search. Computational complexity and simulation of rare events of Ising spin glasses
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]]> 1480 2 http://cdn.slidesharecdn.com/computational-complexity-and-simulation-of-rare-events-of-ising-spin-glasses4173-thumbnail-2?1190721002 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted The Bayesian Optimization Algorithm with Substructural Local Search http://www.slideshare.net/pelikan/the-bayesian-optimization-algorithm-with-substructural-local-search
This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.]]>
This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.]]> Wed, 19 Sep 2007 03:06:23 GMT http://www.slideshare.net/pelikan/the-bayesian-optimization-algorithm-with-substructural-local-search pelikan@slideshare.net(pelikan) The Bayesian Optimization Algorithm with Substructural Local Search pelikan This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups. <img src="http://cdn.slidesharecdn.com/the-bayesian-optimization-algorithm-with-substructural-local-search4950-thumbnail-2?1190171183" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups. The Bayesian Optimization Algorithm with Substructural Local Search
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]]> 1766 0 http://cdn.slidesharecdn.com/the-bayesian-optimization-algorithm-with-substructural-local-search4950-thumbnail-2?1190171183 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Analyzing probabilistic models in hierarchical BOA on traps and spin glasses http://www.slideshare.net/kknsastry/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.]]>
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.]]> Wed, 16 May 2007 15:29:52 GMT http://www.slideshare.net/kknsastry/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses kknsastry@slideshare.net(kknsastry) Analyzing probabilistic models in hierarchical BOA on traps and spin glasses kknsastry The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem. <img src="http://cdn.slidesharecdn.com/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses-4915-thumbnail-2?1179329392" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem. Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
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]]> 831 0 http://cdn.slidesharecdn.com/analyzing-probabilistic-models-in-hierarchical-boa-on-traps-and-spin-glasses-4915-thumbnail-2?1179329392 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms http://www.slideshare.net/kknsastry/efficiency-enhancement-of-probabilistic-model-building-genetic-algorithms
This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the probabilistic model of variable interactions. The fitness values of some offspring are estimated using the probabilistic model, thereby avoiding computationally expensive function evaluations. The scalability of the aforementioned techniques are analyzed using facetwise models for convergence time and population sizing. The speed-up obtained by each of the methods is predicted and verified with empirical results. The results show that for additively separable problems the competent mutation operator requires O(k0.5logm)—where k is the building-block size, and m is the number of building blocks—less function evaluations than its selectorecombinative counterpart. The results also show that the use of an internal probabilistic fitness model reduces the required number of function evaluations to as low as 1-10% and yields a speed-up of 2–50.]]>
This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the probabilistic model of variable interactions. The fitness values of some offspring are estimated using the probabilistic model, thereby avoiding computationally expensive function evaluations. The scalability of the aforementioned techniques are analyzed using facetwise models for convergence time and population sizing. The speed-up obtained by each of the methods is predicted and verified with empirical results. The results show that for additively separable problems the competent mutation operator requires O(k0.5logm)—where k is the building-block size, and m is the number of building blocks—less function evaluations than its selectorecombinative counterpart. The results also show that the use of an internal probabilistic fitness model reduces the required number of function evaluations to as low as 1-10% and yields a speed-up of 2–50.]]> Sat, 17 Feb 2007 23:24:10 GMT http://www.slideshare.net/kknsastry/efficiency-enhancement-of-probabilistic-model-building-genetic-algorithms kknsastry@slideshare.net(kknsastry) Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms kknsastry This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the probabilistic model of variable interactions. The fitness values of some offspring are estimated using the probabilistic model, thereby avoiding computationally expensive function evaluations. The scalability of the aforementioned techniques are analyzed using facetwise models for convergence time and population sizing. The speed-up obtained by each of the methods is predicted and verified with empirical results. The results show that for additively separable problems the competent mutation operator requires O(k0.5logm)—where k is the building-block size, and m is the number of building blocks—less function evaluations than its selectorecombinative counterpart. The results also show that the use of an internal probabilistic fitness model reduces the required number of function evaluations to as low as 1-10% and yields a speed-up of 2–50. <img src="http://cdn.slidesharecdn.com/efficiency-enhancement-of-probabilistic-model-building-genetic-algorithms-6530-thumbnail-2?1231919457" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the probabilistic model of variable interactions. The fitness values of some offspring are estimated using the probabilistic model, thereby avoiding computationally expensive function evaluations. The scalability of the aforementioned techniques are analyzed using facetwise models for convergence time and population sizing. The speed-up obtained by each of the methods is predicted and verified with empirical results. The results show that for additively separable problems the competent mutation operator requires O(k0.5logm)—where k is the building-block size, and m is the number of building blocks—less function evaluations than its selectorecombinative counterpart. The results also show that the use of an internal probabilistic fitness model reduces the required number of function evaluations to as low as 1-10% and yields a speed-up of 2–50. Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms
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]]> 1638 0 http://cdn.slidesharecdn.com/efficiency-enhancement-of-probabilistic-model-building-genetic-algorithms-6530-thumbnail-2?1231919457 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Sporadic Model Building for Efficiency Enhancement of hBOA http://www.slideshare.net/kknsastry/sporadic-model-building-for-efficiency-enhancement-of-hboa
This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs). With sporadic model building, the structure of the probabilistic model is updated once every few iterations (generations), whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup that decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n0.26) to Θ(n0.65), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, for decomposable problems, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building.]]>
This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs). With sporadic model building, the structure of the probabilistic model is updated once every few iterations (generations), whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup that decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n0.26) to Θ(n0.65), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, for decomposable problems, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building.]]> Fri, 16 Feb 2007 23:04:39 GMT http://www.slideshare.net/kknsastry/sporadic-model-building-for-efficiency-enhancement-of-hboa kknsastry@slideshare.net(kknsastry) Sporadic Model Building for Efficiency Enhancement of hBOA kknsastry This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs). With sporadic model building, the structure of the probabilistic model is updated once every few iterations (generations), whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup that decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n0.26) to Θ(n0.65), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, for decomposable problems, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. <img src="http://cdn.slidesharecdn.com/sporadic-model-building-for-efficiency-enhancement-of-hboa-20768-thumbnail-2?1231919450" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs). With sporadic model building, the structure of the probabilistic model is updated once every few iterations (generations), whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup that decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n0.26) to Θ(n0.65), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, for decomposable problems, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. Sporadic Model Building for Efficiency Enhancement of hBOA
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]]> 1152 0 http://cdn.slidesharecdn.com/sporadic-model-building-for-efficiency-enhancement-of-hboa-20768-thumbnail-2?1231919450 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Hierarchical BOA on Random Decomposable Problems http://www.slideshare.net/kknsastry/hierarchical-boa-on-random-decomposable-problems
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems scalably and reliably. This paper describes a class of random additively decomposable problems with and without interactions between the subproblems and tests hBOA on a large number of random instances of the proposed class of problems. The performance of hBOA is compared to that of the simple genetic algorithm with standard crossover and mutation operators, the univariate marginal distribution algorithm, and the hill climbing with bit-flip mutation. The results confirm that hBOA achieves quadratic or subquadratic performance on the proposed class of random decomposable problems and that it significantly outperforms all other methods included in the comparison. The results also show that low-order polynomial scalability is retained even when only a small percentage of hardest problems are considered and that hBOA is a robust algorithm because its performance does not change much across the entire spectrum of random problem instances of the same structure. The proposed class of decomposable problems can be used to test other optimization algorithms that address nearly decomposable problems.]]>
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems scalably and reliably. This paper describes a class of random additively decomposable problems with and without interactions between the subproblems and tests hBOA on a large number of random instances of the proposed class of problems. The performance of hBOA is compared to that of the simple genetic algorithm with standard crossover and mutation operators, the univariate marginal distribution algorithm, and the hill climbing with bit-flip mutation. The results confirm that hBOA achieves quadratic or subquadratic performance on the proposed class of random decomposable problems and that it significantly outperforms all other methods included in the comparison. The results also show that low-order polynomial scalability is retained even when only a small percentage of hardest problems are considered and that hBOA is a robust algorithm because its performance does not change much across the entire spectrum of random problem instances of the same structure. The proposed class of decomposable problems can be used to test other optimization algorithms that address nearly decomposable problems.]]> Thu, 15 Feb 2007 18:05:08 GMT http://www.slideshare.net/kknsastry/hierarchical-boa-on-random-decomposable-problems kknsastry@slideshare.net(kknsastry) Hierarchical BOA on Random Decomposable Problems kknsastry The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems scalably and reliably. This paper describes a class of random additively decomposable problems with and without interactions between the subproblems and tests hBOA on a large number of random instances of the proposed class of problems. The performance of hBOA is compared to that of the simple genetic algorithm with standard crossover and mutation operators, the univariate marginal distribution algorithm, and the hill climbing with bit-flip mutation. The results confirm that hBOA achieves quadratic or subquadratic performance on the proposed class of random decomposable problems and that it significantly outperforms all other methods included in the comparison. The results also show that low-order polynomial scalability is retained even when only a small percentage of hardest problems are considered and that hBOA is a robust algorithm because its performance does not change much across the entire spectrum of random problem instances of the same structure. The proposed class of decomposable problems can be used to test other optimization algorithms that address nearly decomposable problems. <img src="http://cdn.slidesharecdn.com/hierarchical-boa-on-random-decomposable-problems-3052-thumbnail-2?1231919439" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems scalably and reliably. This paper describes a class of random additively decomposable problems with and without interactions between the subproblems and tests hBOA on a large number of random instances of the proposed class of problems. The performance of hBOA is compared to that of the simple genetic algorithm with standard crossover and mutation operators, the univariate marginal distribution algorithm, and the hill climbing with bit-flip mutation. The results confirm that hBOA achieves quadratic or subquadratic performance on the proposed class of random decomposable problems and that it significantly outperforms all other methods included in the comparison. The results also show that low-order polynomial scalability is retained even when only a small percentage of hardest problems are considered and that hBOA is a robust algorithm because its performance does not change much across the entire spectrum of random problem instances of the same structure. The proposed class of decomposable problems can be used to test other optimization algorithms that address nearly decomposable problems. Hierarchical BOA on Random Decomposable Problems
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