Janez Brest | University of Maribor (original) (raw)

Papers by Janez Brest

Research paper thumbnail of Large Scale Global Optimization using Differential Evolution with self-adaptation and cooperative co-evolution

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Research paper thumbnail of Dynamic optimization using Self-Adaptive Differential Evolution

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Research paper thumbnail of Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization

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Research paper thumbnail of Population Reduction Differential Evolution with Multiple Mutation Strategies in Real World Industry Challenges

Lecture Notes in Computer Science, 2012

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Research paper thumbnail of Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization

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Research paper thumbnail of Performance comparison of self-adaptive and adaptive differential evolution algorithms

Soft Computing, Jul 4, 2006

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Research paper thumbnail of Parallel Self-Avoiding Walks for a Low-Autocorrelation Binary Sequences Problem

SSRN Electronic Journal

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Research paper thumbnail of Computational Search of Long Skew-symmetric Binary Sequences with High Merit Factors

MENDEL

In this paper, we present a computational search for best-known merit factors of longer binary se... more In this paper, we present a computational search for best-known merit factors of longer binary sequences with an odd length. Finding low autocorrelation binary sequences with optimal or suboptimal merit factors is a very difficult optimization problem. An improved version of the heuristic algorithm is presented and tackled to search for aperiodic binary sequences with good autocorrelation properties. High-performance computations with the execution of our stochastic algorithmto search skew-symmetric binary sequences with high merit factors. After experimental work, as results, we present new binary sequences with odd lengths between 201 and 303 that are skew-symmetric and have the merit factor F greater than 8.5. Moreover, an example of a binary sequence having F > 8 has been found for all odd lengths between 201 and 303. The longest binary sequence with F > 9 found to date is of length 255.

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Research paper thumbnail of In Searching of Long Skew-symmetric Binary Sequences with High Merit Factors

ArXiv, 2020

In this paper we present best-known merit factors of longer binary sequences with odd length. Fin... more In this paper we present best-known merit factors of longer binary sequences with odd length. Finding low autocorrelation binary sequences with optimal merit factors is difficult optimization problem. High performance computations with execution of a stochastic algorithm in parallel, enable us searching skew-symmetric binary sequences with high merit factors. After experimental work, as results we present sequences with odd length between 301 and 401 that are skew-symmetric and have merit factor F greater than 7. Moreover, now all sequences with odd length between 301 and 401 with F > 7 have been found.

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Research paper thumbnail of Multi-objective Numerically Coded Procedural Tree Models Reconstruction by Differential Evolution

This paper presents an approach for multi-objective reconstruction of procedural models for woody... more This paper presents an approach for multi-objective reconstruction of procedural models for woody plants (trees). The tree model works by building the 3D structure of a tree by applying a fixed procedure on a given set of numerically coded input parameters to recursively compute building parts of a tree. The parametrized procedural model can later be used for computer animation. Reconstruction of a parametrized procedural model from the photographic images is done by differential evolution algorithm, which evolves the parametrized procedural model by fitting a set of its rendered images to a set of given photographic images. The two-objective comparisons are made on a pixel level of the images by integrating distances to nearest similar pixels. The obtained results show that the presented approach is viable for modeling of natural trees in computer animation by multi-objective evolution of the numerically coded procedural model. The use of multi-objective approach gives the decision...

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Research paper thumbnail of Using Differential Evolution for the Graph Coloring

Differential evolution was developed for reliable and versatile function optimization. It has als... more Differential evolution was developed for reliable and versatile function optimization. It has also become interesting for other domains because of its ease to use. In this paper, we posed the question of whether differential evolution can also be used by solving of the combinatorial optimization problems, and in particular, for the graph coloring problem. Therefore, a hybrid self-adaptive differential evolution algorithm for graph coloring was proposed that is comparable with the best heuristics for graph coloring today, i.e. Tabucol of Hertz and de Werra and the hybrid evolutionary algorithm of Galinier and Hao. We have focused on the graph 3-coloring. Therefore, the evolutionary algorithm with method SAW of Eiben et al., which achieved excellent results for this kind of graphs, was also incorporated into this study. The extensive experiments show that the differential evolution could become a competitive tool for the solving of graph coloring problem in the future.

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Research paper thumbnail of Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization

This paper presents a novel Differential Evolution algorithm for protein folding optimization tha... more This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our...

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Research paper thumbnail of Low-Autocorrelation Binary Sequences: On Improved Merit Factors and Runtime Predictions to Achieve Them

The search for binary sequences with a high figure of merit, known as the low autocorrelation bin... more The search for binary sequences with a high figure of merit, known as the low autocorrelation binary sequence (labs) problem, represents a formidable computational challenge. To mitigate the computational constraints of the problem, we consider solvers that accept odd values of sequence length L and return solutions for skew-symmetric binary sequences only -- with the consequence that not all best solutions under this constraint will be optimal for each L. In order to improve both, the search for best merit factor and the asymptotic runtime performance, we instrumented three stochastic solvers, the first two are state-of-the-art solvers that rely on variants of memetic and tabu search (lssMAts and lssRRts), the third solver (lssOrel) organizes the search as a sequence of independent contiguous self-avoiding walk segments. By adapting a rigorous statistical methodology to performance testing of all three combinatorial solvers, experiments show that the solver with the best asymptotic...

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Research paper thumbnail of Hybridization of Evolutionary Algorithms

Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific... more Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized. In this chapter, the hybridization of the three elements of the evolutionary algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function, the existing heuristic function that construct the solution of the problem in traditional way is used. However, this function is embedded into the evolutionary algorithm that serves as a generator of new solutions. In addition, the objective function is improved by local search heuristics. The new neutral selection operator has been developed that is capable to deal with neutral solutions, i.e. solutions that have the different representation but expose the equal values of objective function. ...

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Research paper thumbnail of A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring

The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the... more The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the intelligent behavior of a honey bee swarm. It is widely recognized as a quick, reliable, and efficient methods for solving optimization problems. This paper proposes a hybrid ABC (HABC) algorithm for graph 3-coloring, which is a well-known discrete optimization problem. The results of HABC are compared with results of the well-known graph coloring algorithms of today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive experimentations has shown that the HABC matched the competitive results of the best graph coloring algorithms, and did better than the traditional heuristics EA-SAW when solving equi-partite, flat, and random generated medium-sized graphs.

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Research paper thumbnail of English Edition

Abstract. Umko is a strong open-source chess program developed to collect good concepts from lite... more Abstract. Umko is a strong open-source chess program developed to collect good concepts from literature and other open-source projects. Using these concepts, we want to implement an optimally chess program. To do this, Umko has implemented a bitboard representation, move generator, parallel search algorithm, multiple principal variation search, transposition table, universal chess interface, evaluation function, usage of endgame tablebases and usage of the opening book. The paper provides details of these concepts. Umko is a program running on several platforms inside different graphical user interfaces and using the modern processor technology. It has a parallel search algorithm allowing its program to simultaneously use more processors or cores and the new SSE4.2 CPU instruction set. Both the parallel search algorithm and the new instruction set enable the program to be a faster and stronger player. Having been tested on different independent rating lists, the program is rated amo...

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Research paper thumbnail of Towards the novel reasoning among particles in pso by the use of rdf and sparql,”The Scientific World

The significant development of the Internet has posed some new challenges and many new programmin... more The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.

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Research paper thumbnail of ENGLISH EDITION A Brief Review of Nature-Inspired Algorithms for Optimization

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Research paper thumbnail of Clustering and differential evolution for multimodal optimization

2017 IEEE Congress on Evolutionary Computation (CEC)

This paper presents a new differential evolution algorithm for multimodal optimization that uses ... more This paper presents a new differential evolution algorithm for multimodal optimization that uses self-adaptive parameter control, clustering and crowding methods. The algorithm includes a new clustering mechanism that is based on small subpopulations with the best strategy and, as such, improves the algorithm's efficiency. Each subpopulation is generated according to the best individual from a population that is not added to any other subpopulation. These small subpopulations are also used to determine population size and to replace ‘bad’ individuals. Because of the small subpopulation size and crowding mechanism, bad individuals prevent the best individuals from converging to the optimum. Therefore, the algorithm is trying to replace bad individuals with the individuals that are close to the best individuals. The population size expansion is used within the algorithm according to the number of generated subpopulations and located optima. The proposed algorithm was tested on benchmark functions for CEC'2013 special session and competition on niching methods for multimodal function optimization. The performance of the proposed algorithm was comparable with the state-of-the-art algorithms.

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Research paper thumbnail of On asymptotic complexity of the optimum Golomb ruler problem: From established stochastic methods to self-avoiding walks

2017 IEEE Congress on Evolutionary Computation (CEC)

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Research paper thumbnail of Large Scale Global Optimization using Differential Evolution with self-adaptation and cooperative co-evolution

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Dynamic optimization using Self-Adaptive Differential Evolution

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Population Reduction Differential Evolution with Multiple Mutation Strategies in Real World Industry Challenges

Lecture Notes in Computer Science, 2012

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Research paper thumbnail of Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Performance comparison of self-adaptive and adaptive differential evolution algorithms

Soft Computing, Jul 4, 2006

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Research paper thumbnail of Parallel Self-Avoiding Walks for a Low-Autocorrelation Binary Sequences Problem

SSRN Electronic Journal

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Research paper thumbnail of Computational Search of Long Skew-symmetric Binary Sequences with High Merit Factors

MENDEL

In this paper, we present a computational search for best-known merit factors of longer binary se... more In this paper, we present a computational search for best-known merit factors of longer binary sequences with an odd length. Finding low autocorrelation binary sequences with optimal or suboptimal merit factors is a very difficult optimization problem. An improved version of the heuristic algorithm is presented and tackled to search for aperiodic binary sequences with good autocorrelation properties. High-performance computations with the execution of our stochastic algorithmto search skew-symmetric binary sequences with high merit factors. After experimental work, as results, we present new binary sequences with odd lengths between 201 and 303 that are skew-symmetric and have the merit factor F greater than 8.5. Moreover, an example of a binary sequence having F > 8 has been found for all odd lengths between 201 and 303. The longest binary sequence with F > 9 found to date is of length 255.

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Research paper thumbnail of In Searching of Long Skew-symmetric Binary Sequences with High Merit Factors

ArXiv, 2020

In this paper we present best-known merit factors of longer binary sequences with odd length. Fin... more In this paper we present best-known merit factors of longer binary sequences with odd length. Finding low autocorrelation binary sequences with optimal merit factors is difficult optimization problem. High performance computations with execution of a stochastic algorithm in parallel, enable us searching skew-symmetric binary sequences with high merit factors. After experimental work, as results we present sequences with odd length between 301 and 401 that are skew-symmetric and have merit factor F greater than 7. Moreover, now all sequences with odd length between 301 and 401 with F > 7 have been found.

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Research paper thumbnail of Multi-objective Numerically Coded Procedural Tree Models Reconstruction by Differential Evolution

This paper presents an approach for multi-objective reconstruction of procedural models for woody... more This paper presents an approach for multi-objective reconstruction of procedural models for woody plants (trees). The tree model works by building the 3D structure of a tree by applying a fixed procedure on a given set of numerically coded input parameters to recursively compute building parts of a tree. The parametrized procedural model can later be used for computer animation. Reconstruction of a parametrized procedural model from the photographic images is done by differential evolution algorithm, which evolves the parametrized procedural model by fitting a set of its rendered images to a set of given photographic images. The two-objective comparisons are made on a pixel level of the images by integrating distances to nearest similar pixels. The obtained results show that the presented approach is viable for modeling of natural trees in computer animation by multi-objective evolution of the numerically coded procedural model. The use of multi-objective approach gives the decision...

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Research paper thumbnail of Using Differential Evolution for the Graph Coloring

Differential evolution was developed for reliable and versatile function optimization. It has als... more Differential evolution was developed for reliable and versatile function optimization. It has also become interesting for other domains because of its ease to use. In this paper, we posed the question of whether differential evolution can also be used by solving of the combinatorial optimization problems, and in particular, for the graph coloring problem. Therefore, a hybrid self-adaptive differential evolution algorithm for graph coloring was proposed that is comparable with the best heuristics for graph coloring today, i.e. Tabucol of Hertz and de Werra and the hybrid evolutionary algorithm of Galinier and Hao. We have focused on the graph 3-coloring. Therefore, the evolutionary algorithm with method SAW of Eiben et al., which achieved excellent results for this kind of graphs, was also incorporated into this study. The extensive experiments show that the differential evolution could become a competitive tool for the solving of graph coloring problem in the future.

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Research paper thumbnail of Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization

This paper presents a novel Differential Evolution algorithm for protein folding optimization tha... more This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our...

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Research paper thumbnail of Low-Autocorrelation Binary Sequences: On Improved Merit Factors and Runtime Predictions to Achieve Them

The search for binary sequences with a high figure of merit, known as the low autocorrelation bin... more The search for binary sequences with a high figure of merit, known as the low autocorrelation binary sequence (labs) problem, represents a formidable computational challenge. To mitigate the computational constraints of the problem, we consider solvers that accept odd values of sequence length L and return solutions for skew-symmetric binary sequences only -- with the consequence that not all best solutions under this constraint will be optimal for each L. In order to improve both, the search for best merit factor and the asymptotic runtime performance, we instrumented three stochastic solvers, the first two are state-of-the-art solvers that rely on variants of memetic and tabu search (lssMAts and lssRRts), the third solver (lssOrel) organizes the search as a sequence of independent contiguous self-avoiding walk segments. By adapting a rigorous statistical methodology to performance testing of all three combinatorial solvers, experiments show that the solver with the best asymptotic...

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Research paper thumbnail of Hybridization of Evolutionary Algorithms

Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific... more Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized. In this chapter, the hybridization of the three elements of the evolutionary algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function, the existing heuristic function that construct the solution of the problem in traditional way is used. However, this function is embedded into the evolutionary algorithm that serves as a generator of new solutions. In addition, the objective function is improved by local search heuristics. The new neutral selection operator has been developed that is capable to deal with neutral solutions, i.e. solutions that have the different representation but expose the equal values of objective function. ...

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Research paper thumbnail of A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring

The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the... more The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the intelligent behavior of a honey bee swarm. It is widely recognized as a quick, reliable, and efficient methods for solving optimization problems. This paper proposes a hybrid ABC (HABC) algorithm for graph 3-coloring, which is a well-known discrete optimization problem. The results of HABC are compared with results of the well-known graph coloring algorithms of today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive experimentations has shown that the HABC matched the competitive results of the best graph coloring algorithms, and did better than the traditional heuristics EA-SAW when solving equi-partite, flat, and random generated medium-sized graphs.

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Research paper thumbnail of English Edition

Abstract. Umko is a strong open-source chess program developed to collect good concepts from lite... more Abstract. Umko is a strong open-source chess program developed to collect good concepts from literature and other open-source projects. Using these concepts, we want to implement an optimally chess program. To do this, Umko has implemented a bitboard representation, move generator, parallel search algorithm, multiple principal variation search, transposition table, universal chess interface, evaluation function, usage of endgame tablebases and usage of the opening book. The paper provides details of these concepts. Umko is a program running on several platforms inside different graphical user interfaces and using the modern processor technology. It has a parallel search algorithm allowing its program to simultaneously use more processors or cores and the new SSE4.2 CPU instruction set. Both the parallel search algorithm and the new instruction set enable the program to be a faster and stronger player. Having been tested on different independent rating lists, the program is rated amo...

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Research paper thumbnail of Towards the novel reasoning among particles in pso by the use of rdf and sparql,”The Scientific World

The significant development of the Internet has posed some new challenges and many new programmin... more The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of ENGLISH EDITION A Brief Review of Nature-Inspired Algorithms for Optimization

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Clustering and differential evolution for multimodal optimization

2017 IEEE Congress on Evolutionary Computation (CEC)

This paper presents a new differential evolution algorithm for multimodal optimization that uses ... more This paper presents a new differential evolution algorithm for multimodal optimization that uses self-adaptive parameter control, clustering and crowding methods. The algorithm includes a new clustering mechanism that is based on small subpopulations with the best strategy and, as such, improves the algorithm's efficiency. Each subpopulation is generated according to the best individual from a population that is not added to any other subpopulation. These small subpopulations are also used to determine population size and to replace ‘bad’ individuals. Because of the small subpopulation size and crowding mechanism, bad individuals prevent the best individuals from converging to the optimum. Therefore, the algorithm is trying to replace bad individuals with the individuals that are close to the best individuals. The population size expansion is used within the algorithm according to the number of generated subpopulations and located optima. The proposed algorithm was tested on benchmark functions for CEC'2013 special session and competition on niching methods for multimodal function optimization. The performance of the proposed algorithm was comparable with the state-of-the-art algorithms.

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Research paper thumbnail of On asymptotic complexity of the optimum Golomb ruler problem: From established stochastic methods to self-avoiding walks

2017 IEEE Congress on Evolutionary Computation (CEC)

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