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We consider a marginal distribution genetic model based on crossover of sequences of genes and provide relations between the associated infinite population genetic system and the neural networks. A lower bound on population size i...
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We consider a marginal distribution genetic model based on crossover of sequences of genes and provide relations between the associated infinite population genetic system and the neural networks. A lower bound on population size is exhibited stating that the behavior of the finite population system, in the case of sufficiently large sizes, can be approximated by the behavior of the corresponding infinite population system. Assumptions on fitness and individual chromosomes are provided implying that the behavior of the finite population genetic system remains consistent with the behavior of the associated infinite population genetic system for suitably long trajectories. The attractors (with binary components) of the infinite population genetic system are characterized as equilibrium points of a discrete (neural network) system that can be considered as a variant of a Hopfield's network; it is shown that the fitness is a Lya-punov function for the variant of the discrete Hopfield's net. Our main result can be summarized by stating that the relation between marginal distribution genetic systems and neural nets is much more general than that already shown elsewhere for other simpler models.
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Extensive and computationally complex signal processing and control applications are commonly constructed from small computational blocks where the load decomposition and balance may not be easily achieved. This requires the devel...
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Extensive and computationally complex signal processing and control applications are commonly constructed from small computational blocks where the load decomposition and balance may not be easily achieved. This requires the development of mapping and scheduling strategies based on application to processor matching. In this context several application algorithms are utilised and investigated in this work within the development framework (DF) approach. The DF approach supports the specification, design and implementation of real-time control systems. It also contains several mapping and scheduling tools to improve the performance of systems as well as tools for code generation. To improve the performance of an application, a new approach, namely the priority-based genetic algorithm (PBGA), is developed and reported in this article. The approach is applied to several applications using parallel and distributed heterogeneous architectures and its performance verified in comparison to several previously developed strategies.
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Genetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly conce...
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Genetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly concentrated on the traditional single factory or single job-shop scheduling problems. However, with the increasing popularity of distributed, or globalized production, the previously used GAs are required to be further explored in order to deal with the newly emerged distributed scheduling problems. In this paper, a modified GA is presented, which is capable of solving traditional scheduling problems as well as distributed scheduling problems. Various scheduling objectives can be achieved including minimizing makespan, cost and weighted multiple criteria. The proposed algorithm has been evaluated with satisfactory results through several classical scheduling benchmarks. Furthermore, the capability of the modified GA was also tested for handling the distributed scheduling problems.
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This paper presents a method to partition models in logical processes in the context of distributed simulation. The proposed method uses genetic algorithms to decide on the viability and the partitioning technique most indicated. ...
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This paper presents a method to partition models in logical processes in the context of distributed simulation. The proposed method uses genetic algorithms to decide on the viability and the partitioning technique most indicated. The input parameters to the genetic algorithm are information about the model (number of elements, communication, arrival and service taxes), and the architecture where the simulation is executed. As result, we have the number of logical processes and their mapping on the distributed environment. Two models were used to evaluate the proposed method.
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In mobile communications, a mobile terminal is usually not in a line-of-sight condition from a base station. In this case, signals are assumed to be incident on the base station having angular spread by many scattering objects sur...
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In mobile communications, a mobile terminal is usually not in a line-of-sight condition from a base station. In this case, signals are assumed to be incident on the base station having angular spread by many scattering objects surrounding the mobile terminal. It is important to estimate these distributed sources. We can estimate directions-of-arrival using the MUSIC or ESPRIT algorithm. However, it is difficult to apply the method to distributed sources. In this paper, we propose a scheme to estimate distributed sources using the MUSIC algorithm and genetic algorithm.
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This paper describes a new model of distributed genetic algorithm, "Dual Individual genetic algorithms: Dual DGA". In this algorithm, the subpopulation size is two. The specialized genetic operators which keep the diversity of the...
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This paper describes a new model of distributed genetic algorithm, "Dual Individual genetic algorithms: Dual DGA". In this algorithm, the subpopulation size is two. The specialized genetic operators which keep the diversity of the solutions and contribute the high searching ability are performed in each subpopulation (island). The advantage of this model is that this algorithm has fewer parameters that need to be specified than the traditional distributed genetic algorithm (DGA) has. Through the numerical example, it became cleared that Dual DGA has a higher searching ability compared to the traditional DGA. It is also inferred that the searching method of Dual DGA is different from that of fine-grained model, even when there are two individuals in each island.
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Many real-world optimization problems in the scientific and engineering fields can be solved by genetic algorithms (GAs) but it still requires a long execution time for complex problems. At the same time, there are many under-util...
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Many real-world optimization problems in the scientific and engineering fields can be solved by genetic algorithms (GAs) but it still requires a long execution time for complex problems. At the same time, there are many under-utilized workstations on the Internet. In this paper, we present a sell-adaptive parallel GA system named APGAIN, which utilizes the spare power of the heterogeneous workstations on the Internet to solve complex optimization problems. In order to maintain a balance between exploitation and exploration, we have devised a novel probabilistic rule-driven adaptive model (PRDAM) to adapt the GA parameters automatically. APGAIN is implemented on an Internet Computing system culled DJM. In the implementation, we discover that DJM's original load balancing strategy is insufficient. Hence the strategy is extended with the job migration capability. The performance of the system is evaluated by solving the traveling salesman problem with data from a public database.
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The exploration efficiency of GAs depends on parameter values such as the mutation rate and crossover rate. To save the labor of manually adjusting these values, GAs which automatically adjust parameters (adaptive GAs) have been p...
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The exploration efficiency of GAs depends on parameter values such as the mutation rate and crossover rate. To save the labor of manually adjusting these values, GAs which automatically adjust parameters (adaptive GAs) have been proposed. However, most of the existing adaptive GAs can adjust only a few parameters simultaneously. Although several adaptive GAs can adjust many parameters simultaneously, these algorithms have a large computational cost. In this paper, we propose the Self-Adaptive Island GA (SAIGA) and its asynchronous implementation Asynchronous SAIGA (A-SAIGA). These two GAs are combinations of Meta GA and Island GA, and can adapt many parameters simultaneously with a computational cost equivalent to that of the simple GA. A-SAIGA improves exploration speed by avoiding synchronization between islands. Throughout our evaluation experiments, we confirmed that the performance of these GAs is close to that of the simple GA with optimal parameters. We also confirmed that A-SAIGA outperforms SAIGA in exploration speed.
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This paper considers implementation of the chromosome exchange based oh the hierarchical ring topology in parallel distributed genetic algorithms. As target systems, we treat multiprocessor systems with mesh ring topology and bus-...
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This paper considers implementation of the chromosome exchange based oh the hierarchical ring topology in parallel distributed genetic algorithms. As target systems, we treat multiprocessor systems with mesh ring topology and bus-connected ones. The proposed method tries to save the communication overhead.
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