Slideshows for Tag: efficiency-enhancement http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows for Tag: efficiency-enhancement http://www.slideshare.net/ Tue, 22 Jul 2008 08:39:41 GMT SlideShare feed for Slideshows for Tag: efficiency-enhancement Using Previous Models to Bias Structural Learning in the Hierarchical BOA http://www.slideshare.net/pelikan/using-previous-models-to-bias-structural-learning-in-the-hierarchical-boa
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.]]>
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.]]> Tue, 22 Jul 2008 08:39:41 GMT http://www.slideshare.net/pelikan/using-previous-models-to-bias-structural-learning-in-the-hierarchical-boa pelikan@slideshare.net(pelikan) Using Previous Models to Bias Structural Learning in the Hierarchical BOA pelikan Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure. <img src="http://cdn.slidesharecdn.com/g08biashauschildfinal-1216723580077074-9-thumbnail-2?1216715983" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure. Using Previous Models to Bias Structural Learning in the Hierarchical BOA
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]]> 941 0 http://cdn.slidesharecdn.com/g08biashauschildfinal-1216723580077074-9-thumbnail-2?1216715983 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 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 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 Genetic Algorithms and Genetic Programming for Multiscale Modeling http://www.slideshare.net/kknsastry/genetic-algorithms-and-genetic-programming-for-multiscale-modeling
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.]]>
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.]]> Thu, 13 Sep 2007 00:20:39 GMT http://www.slideshare.net/kknsastry/genetic-algorithms-and-genetic-programming-for-multiscale-modeling kknsastry@slideshare.net(kknsastry) Genetic Algorithms and Genetic Programming for Multiscale Modeling kknsastry 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. <img src="http://cdn.slidesharecdn.com/genetic-algorithms-and-genetic-programming-for-multiscale-modeling2802-thumbnail-2?1189642839" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> 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. Genetic Algorithms and Genetic Programming for Multiscale Modeling
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]]> 2051 0 http://cdn.slidesharecdn.com/genetic-algorithms-and-genetic-programming-for-multiscale-modeling2802-thumbnail-2?1189642839 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability? http://www.slideshare.net/pelikan/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability
It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible.]]>
It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible.]]> Thu, 26 Jul 2007 05:23:22 GMT http://www.slideshare.net/pelikan/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability pelikan@slideshare.net(pelikan) Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability? pelikan It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible. <img src="http://cdn.slidesharecdn.com/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability1657-thumbnail-2?1185427402" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This presentation demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible. Order Or Not: Does Parallelization of Model Building in hBOA Affect Its Scalability?
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]]> 850 0 http://cdn.slidesharecdn.com/order-or-not-does-parallelization-of-model-building-in-hboa-affect-its-scalability1657-thumbnail-2?1185427402 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Towards billion bit optimization via parallel estimation of distribution algorithm http://www.slideshare.net/kknsastry/towards-billion-bit-optimization-via-parallel-estimation-of-distribution-algorithm
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 billion-variable problems across the landscape of search problems.]]>
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 billion-variable problems across the landscape of search problems.]]> Sat, 14 Jul 2007 12:49:14 GMT http://www.slideshare.net/kknsastry/towards-billion-bit-optimization-via-parallel-estimation-of-distribution-algorithm kknsastry@slideshare.net(kknsastry) Towards billion bit optimization via parallel estimation of distribution algorithm kknsastry 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 billion-variable problems across the landscape of search problems. <img src="http://cdn.slidesharecdn.com/towards-billion-bit-optimization-via-parallel-estimation-of-distribution-algorithm1292-thumbnail-2?1184417354" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> 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 billion-variable problems across the landscape of search problems. Towards billion bit optimization via parallel estimation of distribution algorithm
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]]> 1858 0 http://cdn.slidesharecdn.com/towards-billion-bit-optimization-via-parallel-estimation-of-distribution-algorithm1292-thumbnail-2?1184417354 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Reflections of a Keen Modeler http://www.slideshare.net/deg511/reflections-of-a-keen-modeler
OBUPM-2007 presentation by David E. Goldberg]]>
OBUPM-2007 presentation by David E. Goldberg]]> Wed, 11 Jul 2007 15:11:53 GMT http://www.slideshare.net/deg511/reflections-of-a-keen-modeler deg511@slideshare.net(deg511) Reflections of a Keen Modeler deg511 OBUPM-2007 presentation by David E. Goldberg <img src="http://cdn.slidesharecdn.com/reflections-of-a-keen-modeler4353-thumbnail-2?1184166713" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> OBUPM-2007 presentation by David E. Goldberg Reflections of a Keen Modeler
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]]> 1364 0 http://cdn.slidesharecdn.com/reflections-of-a-keen-modeler4353-thumbnail-2?1184166713 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Silicon Cluster Optimization Using Extended Compact Genetic Algorithm http://www.slideshare.net/kknsastry/silicon-cluster-optimization-using-extended-compact-genetic-algorithm
This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation.]]>
This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation.]]> Sun, 25 Feb 2007 16:57:10 GMT http://www.slideshare.net/kknsastry/silicon-cluster-optimization-using-extended-compact-genetic-algorithm kknsastry@slideshare.net(kknsastry) Silicon Cluster Optimization Using Extended Compact Genetic Algorithm kknsastry This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation. <img src="http://cdn.slidesharecdn.com/silicon-cluster-optimization-using-extended-compact-genetic-algorithm-16908-thumbnail-2?1231919536" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation. Silicon Cluster Optimization Using Extended Compact Genetic Algorithm
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]]> 2381 0 http://cdn.slidesharecdn.com/silicon-cluster-optimization-using-extended-compact-genetic-algorithm-16908-thumbnail-2?1231919536 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algorithm with A Seeded Population http://www.slideshare.net/kknsastry/efficient-cluster-optimization-using-a-hybrid-extended-compact-genetic-algorithm-with-a-seeded-population
A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively.]]>
A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively.]]> Sun, 25 Feb 2007 16:27:31 GMT http://www.slideshare.net/kknsastry/efficient-cluster-optimization-using-a-hybrid-extended-compact-genetic-algorithm-with-a-seeded-population kknsastry@slideshare.net(kknsastry) Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algorithm with A Seeded Population kknsastry A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively. <img src="http://cdn.slidesharecdn.com/efficient-cluster-optimization-using-a-hybrid-extended-compact-genetic-algorithm-with-a-seeded-population-14673-thumbnail-2?1231919534" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively. Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algorithm with A Seeded Population
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]]> 2646 0 http://cdn.slidesharecdn.com/efficient-cluster-optimization-using-a-hybrid-extended-compact-genetic-algorithm-with-a-seeded-population-14673-thumbnail-2?1231919534 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Efficiency Enhancement of Genetic Algorithms Via Building-Block-Wise Fitness Estimation http://www.slideshare.net/kknsastry/efficiency-enhancement-of-genetic-algorithms-via-buildingblockwise-fitness-estimation
This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75—2.25.]]>
This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75—2.25.]]> Sat, 17 Feb 2007 23:28:02 GMT http://www.slideshare.net/kknsastry/efficiency-enhancement-of-genetic-algorithms-via-buildingblockwise-fitness-estimation kknsastry@slideshare.net(kknsastry) Efficiency Enhancement of Genetic Algorithms Via Building-Block-Wise Fitness Estimation kknsastry This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75—2.25. <img src="http://cdn.slidesharecdn.com/efficiency-enhancement-of-genetic-algorithms-via-buildingblockwise-fitness-estimation-28867-thumbnail-2?1231919458" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75—2.25. Efficiency Enhancement of Genetic Algorithms Via Building-Block-Wise Fitness Estimation
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]]> 1178 0 http://cdn.slidesharecdn.com/efficiency-enhancement-of-genetic-algorithms-via-buildingblockwise-fitness-estimation-28867-thumbnail-2?1231919458 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 Evaluation Relaxation Using Substructural Information and Linear Estimation http://www.slideshare.net/kknsastry/evaluation-relaxation-using-substructural-information-and-linear-estimation
The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75–3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio.]]>
The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75–3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio.]]> Thu, 15 Feb 2007 20:27:41 GMT http://www.slideshare.net/kknsastry/evaluation-relaxation-using-substructural-information-and-linear-estimation kknsastry@slideshare.net(kknsastry) Evaluation Relaxation Using Substructural Information and Linear Estimation kknsastry The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75–3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio. <img src="http://cdn.slidesharecdn.com/evaluation-relaxation-using-substructural-information-and-linear-estimation-29397-thumbnail-2?1231919440" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75–3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio. Evaluation Relaxation Using Substructural Information and Linear Estimation
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]]> 1215 0 http://cdn.slidesharecdn.com/evaluation-relaxation-using-substructural-information-and-linear-estimation-29397-thumbnail-2?1231919440 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Efficiency Enhancement of Estimation of Distribution Algorithms http://www.slideshare.net/kknsastry/efficiency-enhancement-of-estimation-of-distribution-algorithms
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]]> Wed, 14 Feb 2007 17:33:04 GMT http://www.slideshare.net/kknsastry/efficiency-enhancement-of-estimation-of-distribution-algorithms kknsastry@slideshare.net(kknsastry) Efficiency Enhancement of Estimation of Distribution Algorithms kknsastry <img src="http://cdn.slidesharecdn.com/efficiency-enhancement-of-estimation-of-distribution-algorithms-2155-thumbnail-2?1231919435" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Efficiency Enhancement of Estimation of Distribution Algorithms
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