Slideshows for Tag: fitness-inheritance http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows for Tag: fitness-inheritance http://www.slideshare.net/ Tue, 25 Sep 2007 11:57:08 GMT SlideShare feed for Slideshows for Tag: fitness-inheritance 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 Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques http://www.slideshare.net/xllora/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.]]>
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.]]> Wed, 18 Jul 2007 01:28:48 GMT http://www.slideshare.net/xllora/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques xllora@slideshare.net(xllora) Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques xllora A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules. <img src="http://cdn.slidesharecdn.com/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques4969-thumbnail-2?1184722128" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules. Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques
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]]> 1612 0 http://cdn.slidesharecdn.com/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques4969-thumbnail-2?1184722128 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Don&rsquo;t Evaluate, Inherit http://www.slideshare.net/kknsastry/dont-evaluate-inherit
This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique.]]>
This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique.]]> Sun, 25 Feb 2007 16:36:02 GMT http://www.slideshare.net/kknsastry/dont-evaluate-inherit kknsastry@slideshare.net(kknsastry) Don&rsquo;t Evaluate, Inherit kknsastry This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique. <img src="http://cdn.slidesharecdn.com/dont-evaluate-inherit-14555-thumbnail-2?1231919535" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique. Don't Evaluate, Inherit
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]]> 1212 0 http://cdn.slidesharecdn.com/dont-evaluate-inherit-14555-thumbnail-2?1231919535 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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