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The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, befor...
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... generalized chromosome structure proposed in Ref. [8] is shown in Fig. 1, where ^ m and ~ m are numbers of super vertices and scattering vertices, respectively. Generally, the GC can be used to code the GTSP and CTSP hybrid prob- lems, which means that there are vertex groups (the super vertices) as well as the ordinary vertices (the scattering ver- tices). The readers can refer to Ref. [8] for the detailed ...
Context 2
... the length of GC as l. It can be known that, from Eq. (4) and the GC structure shown in Fig. ...
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The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, befor...
The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, befor...
The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, befor...
The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, befor...
The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, befor...
Citations
... MARL+2opt (2015) is a new multiagent reinforcement learning algorithm [42]. HGA [43] (2014), GA+2nd [43], and GCGA [44] (2008) are the improved genetic algorithms. ASA-GS (2011) is an adaptive simulated annealing algorithm [45]. ...
To address the lack of convergence speed and diversity of Ant Colony Optimization (ACO), a dynamic reproductive ant colony algorithm based on piecewise clustering (RCACS) is proposed to optimize the problems. First, the data is segmented by the clustering algorithm, and the exit and entrance of each cluster are obtained by the nearest neighbor strategy. The algorithm traversed all points of the cluster to find an unclosed shortest path according to the exit and entrance. Many fragment paths eventually merge into a full TSP. This strategy can accelerate the convergence speed of the algorithm and help it get higher accuracy. Second, when the algorithm stagnates, the dynamic regeneration mechanism based on feature transfer will transfer the excellent features of the mother-ants to the child-ants, so that it can further explore the neighborhood of the current optimal solution and help the algorithm jump out of the local optimum. From the results of simulation experiments and the rank-sum test, it can be found that the improved algorithm can effectively improve the diversity and accuracy of the algorithm, especially when solving large-scale problems.
... MARL+2opt (2015) is a new multiagent reinforcement learning algorithm (Alipour and Razavi 2015). HGA (Wang 2014) (2014), GA+2nd (Wang 2014), and GCGA (Yang et al. 2008(Yang et al. ) (2008 are the improved genetic algorithms. ...
... MARL+2opt (2015) is a new multiagent reinforcement learning algorithm (Alipour and Razavi 2015). HGA (Wang 2014) (2014), GA+2nd (Wang 2014), and GCGA (Yang et al. 2008(Yang et al. ) (2008 are the improved genetic algorithms. ...
To deal with the problems that the ant colony algorithm has slow convergence speed and easy falling into the local optimum when solving TSP, an ant colony algorithm based on magnetic neighborhood and filtering recommendation (MRACS) is proposed to solve these problems. First, a dynamic magnetic neighborhood strategy is adopted to balance the convergence speed and the solution accuracy by magnetic attraction. It attracts ants to enlarge the exploration of a better neighborhood, thus improving the accuracy of the result. Second, a cross-excitation strategy based on filtering recommendation is applied to increase the diversity of the algorithm by dynamic weakening or enhancing local pheromones in the neighborhoods. It aids the algorithm get rid of the local optimum. Through the simulation experiments and the rank-sum test analysis, it is observed that the MRACS can effectively balance the convergence speed and the accuracy of the solution.
... The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. ...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heu-ristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridiza-tion). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.
... The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. ...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation
... The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. ...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation
... The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. ...
... The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. ...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively man-age the searching system strategy. This algorithm is derived from natural se-lection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the im-proved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random test-ing, historical information is provided with intelligent enslavement to contin-ue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.
... The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. ...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.
... Starting from a particular work station, there exist multiple paths for the working crew to traverse available faulty device locations. Each of these faulty meter locations is considered as a gene; and a working path connecting the faulty meter locations is considered as a chromosome [46]. The adaptive genetic algorithm (AGA) generates new series of chromosome using crossover and mutation operations, where crossover operation searches the optimal solution globally; and mutation operation searches the optimal solution locally [35]. ...
Large-scale power grids, especially smart grid systems, consist of a huge amount of complex computerized electronic devices, such as smart meters. A smart maintenance system is desired to schedule and send maintenance worker to locations where any computerized devices become faulty. A grid management system (GMS) is purposely designed in the way that the following three conditions are generally fulfilled: 1) all workers are working on full capacity everyday; 2) the highest severity level faulty devices are fixed the quickest; and 3) the overall traveling distance/time is minimized. In this study, two intelligent grid maintenance framework are proposed considering the above three conditioned based on two state-of-arts algorithms, namely, genetic algorithm and K-mediods clustering method, respectively. Five real-world datasets collected from five different local cities/counties in eastern China are adopted and applied to verify the effectiveness of the two proposed intelligent grid maintenance frameworks.
... The best known LS algorithm for the TSP is the Lin-Kernighan (LK) algorithm (Lin and Kernighan, 1973). Tabu search (Nguyen et al., 2007;Peng et al., 2004), simulated annealing (Pepper et al., 2002;Wang et al., 2004;Geng et al., 2011), genetic algorithms (Magyar et al., 2000;Cheng et al., 2002;Yang et al., 2008), and swarm algorithms (Goldbarg et al., 2008;Wang et al., 2003;Chen and Chien, 2011). ...
... The results presented in Table 4 and Figure 6 show that the accuracy of the MARL+2-opt combined with NICH-LS heuristic is improved greatly and the MARL+NICH-LS is quite promising and it can provide good results in reasonable time for both small size and large size datasets, although the running times are a little greater than MARL+2-opt algorithm. Créput and Koukam (2009) To compare the MARL+NICH-LS with the recent algorithms (Geng et al., 2011;Yang et al., 2008;Chen and Chien, 2011;Masutti and de Castro, 2009;Créput and Koukam, 2009) in term of CPU time, we scale the CPU time of each algorithm by an appropriate scaling coefficient related to their processing systems (Geng et al., 2011), the CPU time and their scaling coefficients are shown in Table 5. We compare the experimental results of the MARL+NICH-LS with five recent algorithms such as the genetic algorithm (GCGA) (Yang et al., 2008), the adaptive simulated annealing algorithm with greedy search (ASA-GS) (Geng et al,, 2011), the self-organising neural network (RABNET-TSP) (Masutti and de Castro, 2009), the memetic neural network (Memetic-SOM) (Créput and Koukam, 2009) and the genetic simulated annealing ant colony system with particle swarm optimisation techniques (GSAP) (Chen and Chien, 2011), shown in Table 6. ...
... Créput and Koukam (2009) To compare the MARL+NICH-LS with the recent algorithms (Geng et al., 2011;Yang et al., 2008;Chen and Chien, 2011;Masutti and de Castro, 2009;Créput and Koukam, 2009) in term of CPU time, we scale the CPU time of each algorithm by an appropriate scaling coefficient related to their processing systems (Geng et al., 2011), the CPU time and their scaling coefficients are shown in Table 5. We compare the experimental results of the MARL+NICH-LS with five recent algorithms such as the genetic algorithm (GCGA) (Yang et al., 2008), the adaptive simulated annealing algorithm with greedy search (ASA-GS) (Geng et al,, 2011), the self-organising neural network (RABNET-TSP) (Masutti and de Castro, 2009), the memetic neural network (Memetic-SOM) (Créput and Koukam, 2009) and the genetic simulated annealing ant colony system with particle swarm optimisation techniques (GSAP) (Chen and Chien, 2011), shown in Table 6. The meanings of the columns in Table 6 are same as those in Table 4 (time is in second and scaled according to Table 5). ...
The travelling salesman problem (TSP) is probably the most famous and extensively studied problem in the field of combinatorial optimisation. This problem is in the fields of logistics, transportation, and distribution. Since the TSP is NP-hard, many heuristics for the TSP have been developed. In this paper, we developed a novel local search heuristic, based on nearest insertion into the convex hull construction heuristic for solving Euclidean TSP. The proposed method, nearest insertion into the convex hull local search (NICH-LS) is used to improve the initial tour, which is taken from a tour construction heuristic, multi-agent reinforcement learning (MARL) heuristic, by locally manipulating the order of nodes in the consecutive partial tours of the initial tour. Changing the order of nodes in a partial tour is done via constructing the NICH tour of these nodes and replacing the partial tour with the modified partial tour, if its length is reduced. The proposed novel local search heuristic is applied to 29 benchmark instances from TSPLIB. The computational results show the efficiency of the proposed local search compared with five state-of-the-art heuristics.
