Slideshows for Tag: evolutionary-computation http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows for Tag: evolutionary-computation http://www.slideshare.net/ Thu, 07 Aug 2008 16:34:10 GMT SlideShare feed for Slideshows for Tag: evolutionary-computation Incorporating Restricted Tournament Replacement in ECGA for Non-Stationary Environments http://www.slideshare.net/clima/incorporating-restricted-tournament-replacement-in-ecga-for-nonstationary-environments
This paper investigates the incorporation of restricted tournament replacement (RTR) in the extended compact genetic algorithm (ECGA) for solving problems with non-stationary optima. RTR is a simple yet efficient niching method used to maintain diversity in a population of individuals. While the original version of RTR uses Hamming distance to quantify similarity between individuals, we propose an alternative substructural distance to enforce the niches. The ECGA that restarts the search after a change of environment is compared with the approach of maintaining diversity, using both versions of RTR. Results on several dynamic decomposable test problems demonstrate the usefulness of maintaining diversity throughout the run over the approach of restarting the search from scratch at each change. Furthermore, by maintaining diversity no additional mechanisms are required to detect the change of environment, which is typically a problem-dependent and non-trivial task.]]>
This paper investigates the incorporation of restricted tournament replacement (RTR) in the extended compact genetic algorithm (ECGA) for solving problems with non-stationary optima. RTR is a simple yet efficient niching method used to maintain diversity in a population of individuals. While the original version of RTR uses Hamming distance to quantify similarity between individuals, we propose an alternative substructural distance to enforce the niches. The ECGA that restarts the search after a change of environment is compared with the approach of maintaining diversity, using both versions of RTR. Results on several dynamic decomposable test problems demonstrate the usefulness of maintaining diversity throughout the run over the approach of restarting the search from scratch at each change. Furthermore, by maintaining diversity no additional mechanisms are required to detect the change of environment, which is typically a problem-dependent and non-trivial task.]]> Thu, 07 Aug 2008 16:34:10 GMT http://www.slideshare.net/clima/incorporating-restricted-tournament-replacement-in-ecga-for-nonstationary-environments clima@slideshare.net(clima) Incorporating Restricted Tournament Replacement in ECGA for Non-Stationary Environments clima This paper investigates the incorporation of restricted tournament replacement (RTR) in the extended compact genetic algorithm (ECGA) for solving problems with non-stationary optima. RTR is a simple yet efficient niching method used to maintain diversity in a population of individuals. While the original version of RTR uses Hamming distance to quantify similarity between individuals, we propose an alternative substructural distance to enforce the niches. The ECGA that restarts the search after a change of environment is compared with the approach of maintaining diversity, using both versions of RTR. Results on several dynamic decomposable test problems demonstrate the usefulness of maintaining diversity throughout the run over the approach of restarting the search from scratch at each change. Furthermore, by maintaining diversity no additional mechanisms are required to detect the change of environment, which is typically a problem-dependent and non-trivial task. <img src="http://cdn.slidesharecdn.com/decgatalk-1218133588332477-8-thumbnail-2?1218126850" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper investigates the incorporation of restricted tournament replacement (RTR) in the extended compact genetic algorithm (ECGA) for solving problems with non-stationary optima. RTR is a simple yet efficient niching method used to maintain diversity in a population of individuals. While the original version of RTR uses Hamming distance to quantify similarity between individuals, we propose an alternative substructural distance to enforce the niches. The ECGA that restarts the search after a change of environment is compared with the approach of maintaining diversity, using both versions of RTR. Results on several dynamic decomposable test problems demonstrate the usefulness of maintaining diversity throughout the run over the approach of restarting the search from scratch at each change. Furthermore, by maintaining diversity no additional mechanisms are required to detect the change of environment, which is typically a problem-dependent and non-trivial task. Incorporating Restricted Tournament Replacement in ECGA for Non-Stationary Environments
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]]> 586 0 http://cdn.slidesharecdn.com/decgatalk-1218133588332477-8-thumbnail-2?1218126850 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted From Mating Pool Distributions to Model Overfitting http://www.slideshare.net/clima/from-mating-pool-distributions-to-model-overfitting
This paper addresses selection as a source of overfitting in Bayesian estimation of distribution algorithms (EDAs). The purpose of the paper is twofold. First, it shows how the selection operator can lead to model overfitting in the Bayesian optimization algorithm (BOA). Second, the metric score that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection.]]>
This paper addresses selection as a source of overfitting in Bayesian estimation of distribution algorithms (EDAs). The purpose of the paper is twofold. First, it shows how the selection operator can lead to model overfitting in the Bayesian optimization algorithm (BOA). Second, the metric score that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection.]]> Thu, 07 Aug 2008 16:30:58 GMT http://www.slideshare.net/clima/from-mating-pool-distributions-to-model-overfitting clima@slideshare.net(clima) From Mating Pool Distributions to Model Overfitting clima This paper addresses selection as a source of overfitting in Bayesian estimation of distribution algorithms (EDAs). The purpose of the paper is twofold. First, it shows how the selection operator can lead to model overfitting in the Bayesian optimization algorithm (BOA). Second, the metric score that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. <img src="http://cdn.slidesharecdn.com/overfittingtalk-1218134185974337-9-thumbnail-2?1218126659" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This paper addresses selection as a source of overfitting in Bayesian estimation of distribution algorithms (EDAs). The purpose of the paper is twofold. First, it shows how the selection operator can lead to model overfitting in the Bayesian optimization algorithm (BOA). Second, the metric score that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. From Mating Pool Distributions to Model Overfitting
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]]> 693 0 http://cdn.slidesharecdn.com/overfittingtalk-1218134185974337-9-thumbnail-2?1218126659 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 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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]]> 943 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 Introduction aux métaheuristiques (3h) http://www.slideshare.net/nojhan/introduction-aux-mtaheuristiques-3h
3 heures de cours d\’introduction aux métaheuristiques. Le cours couvre de la conception à l\’utilisation de ces algorithmes d\’optimisation globale (généralement stochastiques). Présenté à l\’École Nationale des Ponts et Chaussées en avril 2008.]]>
3 heures de cours d\’introduction aux métaheuristiques. Le cours couvre de la conception à l\’utilisation de ces algorithmes d\’optimisation globale (généralement stochastiques). Présenté à l\’École Nationale des Ponts et Chaussées en avril 2008.]]> Mon, 28 Apr 2008 16:50:37 GMT http://www.slideshare.net/nojhan/introduction-aux-mtaheuristiques-3h nojhan@slideshare.net(nojhan) Introduction aux métaheuristiques (3h) nojhan 3 heures de cours d\&rsquo;introduction aux métaheuristiques. Le cours couvre de la conception à l\&rsquo;utilisation de ces algorithmes d\&rsquo;optimisation globale (généralement stochastiques). Présenté à l\&rsquo;École Nationale des Ponts et Chaussées en avril 2008. <img src="http://cdn.slidesharecdn.com/enpcdromtaheuristiquescours-1209408635611744-9-thumbnail-2?1209401437" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> 3 heures de cours d\&rsquo;introduction aux métaheuristiques. Le cours couvre de la conception à l\&rsquo;utilisation de ces algorithmes d\&rsquo;optimisation globale (généralement stochastiques). Présenté à l\&rsquo;École Nationale des Ponts et Chaussées en avril 2008. Introduction aux métaheuristiques (3h)
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]]> 2780 0 http://cdn.slidesharecdn.com/enpcdromtaheuristiquescours-1209408635611744-9-thumbnail-2?1209401437 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Métaheuristiques et applications http://www.slideshare.net/nojhan/mtaheuristiques-et-applications
Présentation générale des métaheuristiques et des problèmes d’optimisation qu’elles peuvent aider à résoudre.]]>
Présentation générale des métaheuristiques et des problèmes d’optimisation qu’elles peuvent aider à résoudre.]]> Thu, 20 Sep 2007 12:31:50 GMT http://www.slideshare.net/nojhan/mtaheuristiques-et-applications nojhan@slideshare.net(nojhan) Métaheuristiques et applications nojhan Présentation générale des métaheuristiques et des problèmes d&rsquo;optimisation qu&rsquo;elles peuvent aider à résoudre. <img src="http://cdn.slidesharecdn.com/mtaheuristiques-et-applications1747-thumbnail-2?1190291510" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Présentation générale des métaheuristiques et des problèmes d&rsquo;optimisation qu&rsquo;elles peuvent aider à résoudre. Métaheuristiques et applications
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]]> 1821 0 http://cdn.slidesharecdn.com/mtaheuristiques-et-applications1747-thumbnail-2?1190291510 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 Dconrails Gecco Presentation http://www.slideshare.net/jjmerelo/dconrails-gecco-presentation
Presentation for browser-based evolutionary computation.]]>
Presentation for browser-based evolutionary computation.]]> Thu, 05 Jul 2007 07:28:37 GMT http://www.slideshare.net/jjmerelo/dconrails-gecco-presentation jjmerelo@slideshare.net(jjmerelo) Dconrails Gecco Presentation jjmerelo Presentation for browser-based evolutionary computation. <img src="http://cdn.slidesharecdn.com/dconrails-gecco-presentation3953-thumbnail-2?1183620517" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Presentation for browser-based evolutionary computation. Dconrails Gecco Presentation
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]]> 2204 1 http://cdn.slidesharecdn.com/dconrails-gecco-presentation3953-thumbnail-2?1183620517 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Dynamic modelling of document streams http://www.slideshare.net/jjmerelo/dynamic-modelling-of-document-streams
Presentation for the GECCO conference]]>
Presentation for the GECCO conference]]> Tue, 03 Jul 2007 14:06:53 GMT http://www.slideshare.net/jjmerelo/dynamic-modelling-of-document-streams jjmerelo@slideshare.net(jjmerelo) Dynamic modelling of document streams jjmerelo Presentation for the GECCO conference <img src="http://cdn.slidesharecdn.com/dynamic-modelling-of-document-streams698-thumbnail-2?1183471613" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Presentation for the GECCO conference Dynamic modelling of document streams
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]]> 645 0 http://cdn.slidesharecdn.com/dynamic-modelling-of-document-streams698-thumbnail-2?1183471613 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Genetic algorithms in search, optimization, and machine learning http://www.slideshare.net/deg511/genetic-algorithms-in-search-optimization-and-machine-learning
Introductory lecture from award-winning UIUC course on genetic algorithms.]]>
Introductory lecture from award-winning UIUC course on genetic algorithms.]]> Mon, 14 May 2007 16:50:56 GMT http://www.slideshare.net/deg511/genetic-algorithms-in-search-optimization-and-machine-learning deg511@slideshare.net(deg511) Genetic algorithms in search, optimization, and machine learning deg511 Introductory lecture from award-winning UIUC course on genetic algorithms. <img src="http://cdn.slidesharecdn.com/genetic-algorithms-in-search-optimization-and-machine-learning-12337-thumbnail-2?1179161456" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Introductory lecture from award-winning UIUC course on genetic algorithms. Genetic algorithms in search, optimization, and machine learning
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]]> 6652 3 http://cdn.slidesharecdn.com/genetic-algorithms-in-search-optimization-and-machine-learning-12337-thumbnail-2?1179161456 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted Design of Innovation: Innovation &amp; Genetic Algorithms http://www.slideshare.net/deg511/design-of-innovation-innovation-genetic-algorithms
This is the introductory module of a short course at the UIUC called The Design of Innovation: Lessons from and for Genetic Algorithms. It covers the material of the book of the same name.]]>
This is the introductory module of a short course at the UIUC called The Design of Innovation: Lessons from and for Genetic Algorithms. It covers the material of the book of the same name.]]> Fri, 11 May 2007 13:49:11 GMT http://www.slideshare.net/deg511/design-of-innovation-innovation-genetic-algorithms deg511@slideshare.net(deg511) Design of Innovation: Innovation &amp; Genetic Algorithms deg511 This is the introductory module of a short course at the UIUC called The Design of Innovation: Lessons from and for Genetic Algorithms. It covers the material of the book of the same name. <img src="http://cdn.slidesharecdn.com/design-of-innovation-innovation-genetic-algorithms-7390-thumbnail-2?1178891351" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> This is the introductory module of a short course at the UIUC called The Design of Innovation: Lessons from and for Genetic Algorithms. It covers the material of the book of the same name. Design of Innovation: Innovation & Genetic Algorithms
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]]> 3525 0 http://cdn.slidesharecdn.com/design-of-innovation-innovation-genetic-algorithms-7390-thumbnail-2?1178891351 presentation http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted PPSN 2004 - 3rd session http://www.slideshare.net/jjmerelo/ppsn-2004-3rd-session
Introduction to papers in the third session of the PPSN 2004 conference]]>
Introduction to papers in the third session of the PPSN 2004 conference]]> Sat, 17 Mar 2007 08:14:04 GMT http://www.slideshare.net/jjmerelo/ppsn-2004-3rd-session jjmerelo@slideshare.net(jjmerelo) PPSN 2004 - 3rd session jjmerelo Introduction to papers in the third session of the PPSN 2004 conference <img src="http://cdn.slidesharecdn.com/ppsn-2004-3rd-session-5614-thumbnail-2?1174119244" alt ="" style="border:1px solid #C3E6D8;float:right;" /><br> Introduction to papers in the third session of the PPSN 2004 conference PPSN 2004 - 3rd session
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