Tanese, R. (1989). Distributed genetic algorithms. In Schaffer, J., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 434--439, San Mateo, CA, USA. Morgan Kaufmann. Home/Search Document Not in Database Summary Related Articles Check |
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Systemic Behavior of Cooperative Search Algorithms - Toulouse, Crainic, al. (1998) (Correct)
....information among search programs is to speed up the exploration of the solution space. However, several researchers have observed that cooperation by sharing information was more than just hardware acceleration (Cohoon all [5] Researchers in the eld of parallel genetic algorithms (Tanase [35,36], Jog and Gucht [21] Cohoon all [5] Munetomo, Takai Sato [31] concluded that cooperation not only produces speed up, but it also profoundly modi es the search pattern of the cooperating programs. They found empirical evidence that the speci cation of how search processes cooperate has an ....
R. Tanese. Distributed Genetic Algorithms. In J.D. Scha er, editor, Proc. Third Int. Conference on Genetic Algorithms and their Applications. Morgan Kaufmann Publishers, 1989.
Explicit Control of Diversity - And Ective Variation (Correct)
....improve parsimony pressure through Pareto selection of tness and tree size by adding a (third) diversity objective. A more implicit control of genetic diversity, by comparison, o er semi isolated sub population, called demes, that are widely used in the area of evolutionary computation (see e.g. [16]) The second objective of this paper refers to the structural distance between a parent program and its o spring, i.e. the variation distance. The change induced by a variation operator on the e ective, i.e. tness relevant, code may di er signi cantly from the amount of absolute change. By ....
R. Tanese, Distributed Genetic Algorithms. In J.D. Scha er (ed.) Proceedings of the Third International Conference on Genetic Algorithms, 434-439, Morgan Kaufmann, San Mateo, CA, 1989.
AIM-GP and Parallelism - Peter Nordin Physical (Correct)
....using demes is the neighborhood model. Here the individual can migrate to more than the closest island, they can move within a predefined neighborhood distance , see Figure 7 (Gorges Schleuter 1989) Figure 7: Neighborhood model There exists several implementations of parallel EAs in research. Tanese (Tanese 1989) used an island model of a GA for a parallel machine. He concluded that migration rates have a decisive influence on the performance of the algorithm and that it is possible to achieve at least linear speed up by adding new nodes. Andre and Koza implemented a parallel GP system on a transputer ....
Tanese, R. (1989) Distributed Genetic Algorithms. In Schaffer, J.D. (ed.), Proceedings of
A Cellular Genetic Algorithm Simulating Predator-Prey.. - Li, Sutherland (2002) (Correct)
....as a result of rapid loss of diversity in the GA population, the search is trapped to sub optimal solutions. Many algorithms have been proposed to help maintain a more diverse population so as to prevent premature convergence. Among them, most popular are based on the idea of spatial separation [2,3,4]. In particular cellular GAs (or fine grained Parallel GAs) have been shown to be very effective in maintaining population diversity [5,6,7] In a cellular GA, individuals are commonly mapped onto a 2 dimensional lattice, with each cell corresponding to an individual. Selection and interaction ....
Tanese, R. (1989), Distributed Genetic Algorithms. In Proceeding of the Third International Conference on Genetic Algorithms, Schaffer, J.D. (Ed.), Morgan Kaufmann Publishers, San Mateo, p.434-439.
An Overview of Evolutionary Algorithms: Practical Issues and.. - Whitley (2001) (4 citations) (Correct)
....could then execute as a normal evolutionary algorithm. It could be a canonical genetic algorithm, evolution strategy or Genitor. But occasionally, perhaps every five generations or so, the subpopulations would swap a few strings. This migration allows subpopulations to share genetic material [52, 20, 42, 44]. Note that the implementation cost is extremely minimal. This model can easily be implemented on a network of workstations and has very minimal communication costs since the migration of individuals between islands is limited. The search in every subpopulation will be somewhat different since ....
R. Tanese. Distributed Genetic Algorithms. In J. D. Schaffer, editor, Proc. of the 3rd Int'l. Conf. on GAs. Morgan Kaufmann, 1989.
The Effects of Varying Population Density in a Fine-grained.. - Li, Kirley (2002) (Correct)
....is carried out in parallel using discrete time steps. Selection and crossover occurs locally within small, overlapping neighbourhoods. Subsequently, good genes slowly diffuse across the lattice. The benefits of isolating subpopulation have long been known in the field of evolutionary algorithms [7] [9] Parallel GAs are known to handle difficult multimodal functions more efficiently than serial GAs [10] However, both the course and fine grained models are artificially constrained. The quality of the search and efficiency of the algorithms can be severely affected by the parallel parameter ....
.... most of these parameters are determined empirically and there is no generally accepted agreement on how to choose them [12] Empirical studies of coarsegrained parallel GAs and various specifications of subpopulation size and number and migration policies can be found in a number of papers [7][10] 13] In the case of the fine grained model, much of the research has concentrated on the effects of neighbour size and shape [14] Sarma and De Jong found that the ratio of the radius of the local neighbourhood to the lattice size could be used as an adjustable parameter to control the ....
Tanese, R. (1989), Distributed Genetic Algorithms. In Proceeding of the Third International Conference on Genetic Algorithms, Schaffer, J.D. (Ed.), Morgan Kaufmann Publishers, San Mateo, pp.434-439.
Using Dynastic Exploring Recombination to Promote Diversity in .. - Cotta, Troya (2000) (Correct)
.... algorithm (which turns to be incapable of producing new promising solutions) and the increasing resampling of solutions [4] with the subsequent waste of computational resources) These problems are usually tackled by means of rising the mutation rate [10] or by using non panmictic populations [17, 19]. Despite both options can be e#ective, they require determining and adjusting several parameters (rate of change of the mutation rate, interconnection topology, migration frequency, etc. This is generally a di#cult step, and in spite of active research being conducted to assist setting these ....
R. Tanese. Distributed genetic algorithms. In J.D. Scha#er, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 434--439, San Mateo, CA, 1989. Morgan Kaufmann.
Explicit Control of Diversity and Eective Variation Distance .. - Brameier, Banzhaf (2002) (5 citations) (Correct)
....improve parsimony pressure through Pareto selection of fitness and tree size by adding a (third) diversity objective. In contrast, a more implicit control of genetic diversity o#er semi isolated sub population, called demes, that are widely used in the area of evolutionary computation (see, e.g. [17, 3]) Only a certain percentage of individuals is allowed here to migrate from a deme into another deme during each generation. The second major objective of this paper refers to the structural distance between a parent program and its o#spring, i.e. the variation distance induced by a variation ....
R. Tanese, Distributed Genetic Algorithms. In J.D. Scha#er (ed.) Proceedings of the Third International Conference on Genetic Algorithms, 434--439, Morgan Kaufmann, San Mateo, CA, 1989.
An Integrated Experimental Environment for.. - White, Lepreau.. (2002) (101 citations) (Correct)
....frequently with the measured latency and loss on the NxN paths, and occasionally updated with bandwidth measurements. This mapping is a variation of the NP hard Quadratic Assignment Problem. To provide an efficient, best effort solution, Netbed s wanassign is implemented as a genetic algorithm [39]. Possible solutions are scored based on how closely they match desired link characteristics. For each solution, a normalized sum of errors squared is found for latency, loss rate, and bandwidth. A geometric mean of the three errors results in an overall score. Wanassign evolves its answer by ....
.... results by an order of magnitude using the following three techniques: less stringent and more clever termination conditions; standard optimization techniques, in particular memoizing; and parallelizing the algorithm, which is practical in either a shared memory multiprocessor or on a cluster [39]. Finally, we expect major additional improvement to come from binning the nodes and links into groups with similar characteristics, dramatically reducing the search space. 5.4 Disk Reloading An important feature of testbed control is the ability to reload the contents of node local disks ....
R. Tanese. The Distributed Genetic Algorithm. In Proc. ICGA '89. Morgan Kaufmann, 1989.
Lowering the Barrier to Wireless and Mobile Experimentation - White, Lepreau, Guruprasad (2002) (8 citations) (Correct)
.... to improve that by an order of magnitude using the following techniques: less stringent and more clever termination conditions; standard optimization techniques, in particular memoizing; and parallelizing the algorithm, which is practical in either a shared memory multiprocessor or on a cluster [21]. In the Island Model, mutations and crossovers can be done in parallel, sharing the best solutions periodically; we estimate synchronizing every few seconds to exchange 1 2KB of data. Other algorithmic approaches may also be relevant. For example, constraint programming [22] a method ....
R. Tanese. The Distributed Genetic Algorithm. In Third International Conference on Genetic Algorithms, pages 434--439. Morgan Kaufmann, 1989.
An Analysis of Diversity in Genetic Programming - Gustafson (2004) (Correct)
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Tanese, R. (1989). Distributed genetic algorithms. In Schaffer, J., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 434--439, San Mateo, CA, USA. Morgan Kaufmann.
Massively Parallel Genetic Algorithms - Ghazfan, Srinivasan, Nolan (1994) (Correct)
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Tanese, R., "Distributed Genetic Algorithms," In Proceedings of the 3rd International Conference on Genetic Algorithms, Schaffer, J. (Editor), Morgan Kaufmann, San Mateo, CA, 1989, pp. 434--440.
Embedding Branch and Bound within Evolutionary Algorithms - Cotta, Troya (Correct)
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R. Tanese, Distributed genetic algorithms, in: J. Scha#er (Ed.), Proceedings of the 3
Evolution of Combinatonial and Sequential On-Line.. - Garvie, Thompson (Correct)
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R. Tanese. Distributed genetic algorithms. In J.D. Schaffer, editor, Proc. of the Third International Conference of Genetic Algorithms, pages 434--439. Morgan Kauffmann, 1989.
Distributed Probabilistic Model-Building Genetic.. - Hiroyasu, Miki, Sano.. (2003) (Correct)
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Tanese, R.: Distributed Genetic Algorithms. Proc. 3rd International Conference on Genetic Algorithms (1989) 434--439
The System for Evolutionary Computing on the Computational.. - Tanimura, Hiroyasu, al. (Correct)
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R.Tanese, Distributed genetic algorithms, Proc. of the 3rd ICGA, 1989, 434-439.
The Differences of Parallel Efficiency between the Two.. - Hiroyasu, Miki, Tanimura (Correct)
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R. Tanese. Distributed genetic algorithms. In Proceedings of 3rd International Conference on Genetic Algorithms, pages 432--439, 1989. 4 Proceedings of the fourth international conference /exhibition of high performance computing in asiapacific region, pp. 945-948 5
Applying Memetic Algorithms to the Analysis of.. - Cotta, Mendes.. (2003) (Correct)
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R. Tanese. Distributed genetic algorithms. In J.D. Scha#er, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 434--439, San Mateo, CA, 1989. Morgan Kaufmann.
Distributed Genetic Algorithms with a New Sharing.. - Multiobjective.. (Correct)
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Tanese, R. (1989) "Distributed Genetic Algorithms " Proc. of 3rd international Conference on Genetic Algorithms, pp.432-439 76
Towards Hybrid Evolutionary Algorithms - Preux, Talbi (1997) (Correct)
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Reiko Tanese. Distributed genetic algorithms. In [36], pages 434--439, 1989.
Asynchronous Peer-To-Peer Communication For Failure.. - Clemente, Defago, Satou (2003) (1 citation) (Correct)
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Reiko Tanese. Distributed genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms, pages 434--439, Madison, USA, 1989.
Structural Optimization by Real-Coded Probabilistic .. - Hiroyasu, Miki.. (Correct)
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Tanese,R. Distributed Genetic Algorithms, Proc. of the 3rd International Conference on Genetic Algorithms, 1989.
Parallel Genetic Algorithms with Distributed-Environment .. - Miki, Hiroyasu, Hatanaka (1999) (6 citations) (Correct)
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Tanese, R. (1989), "Distributed Genetic Algorithms", Proceedings of the International Conference on Genetic Algorithms, pp. 434-439.
Are Multiple Runs of Genetic Algorithms Better than One? - Cantu-Paz, Goldberg (Correct)
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Tanese, R.: Distributed genetic algorithms. In Scha#er, J.D., ed.: Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann (1989) 434--439
Parallel Evolutionary Algorithms in Telecommunications: .. - Alba, Cotta, Chicano.. (2002) (Correct)
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R. Tanese. Distributed genetic algorithms. In J. D. Scha#er, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 434--439. Morgan Kaufmann, 1989.
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