Evolving sorting networks using genetic programming and the rapidly reconfigurable Xilinx 6216 field-programmable gate array

Koza, John R.; Bennett, Forrest H.; Hutchings, Jeffrey L.; Bade, Stephen L.; Keane, Martin A.; Debenath, André

doi:10.1109/acssc.1997.680275

Cited by 15 publications

(10 citation statements)

References 4 publications

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“…Noteworthy results include the work of Koza et al [25], of Juillé [23], who found a 13-input network with 45 comparators in 10 parallel layers in 1995, and of Choi and Moon [11], who combined techniques involving network isomorphism and genetic algorithms to find 60-comparator sorting networks for 16 inputs (a network of this size was first discovered by Green in 1969, as reported in [21]). More recently, Valsalam and Miikkulainen [34] applied notions of symmetry and evolutionary search to find networks with smaller numbers of comparators than those known before for 17, 18, 19, 20, 21 and 22 inputs.…”

mentioning

confidence: 96%

Sorting networks: To the end and back again

Codish

Cruz-Filipe

Ehlers

et al. 2019

Journal of Computer and System Sciences

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New properties of the front and back ends of sorting networks are studied, illustrating their utility when searching for bounds on optimal networks. Search focuses first on the "outsides" of the network and then on the inner part. Previous works focused on properties of the front end to break symmetries in the search. The new, out-side-in, properties shed understanding on how sorting networks sort, and facilitate the computation of new bounds on optimality. We present new, faster, parallel sorting networks for 17-20 inputs. For 17 inputs, we show that no sorting network using less layers exists.

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mentioning

confidence: 96%

Sorting networks: To the end and back again

Codish

Cruz-Filipe

Ehlers

et al. 2019

Journal of Computer and System Sciences

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“…GP has proven to be a flexible method for developing algorithms to solve tasks such as evolving sorting networks (Koza et al 1997;Sekanina and Bidlo 2005) and developing quantum algorithms (Massey, Clark, and Stepney 2005;Spector et al 1999). A common way of implementing a GP system involves using GA techniques to modify a population of trees (Koza 1989(Koza , 1990(Koza , 1992, where each tree is a complete program.…”

Section: Genetic Programmingmentioning

confidence: 99%

Automatic Algorithm Development Using New Reinforcement Programming Techniques

White¹,

Martinez

Rudolph

2012

Computational Intelligence

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Reinforcement Programming (RP) is a new approach to automatically generating algorithms that uses reinforcement learning techniques. This paper introduces the RP approach and demonstrates its use to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Experiments establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. Additionally RP was used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.

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“…Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July [16][17][18][19][20][21]2006 125MHz PA-RISC workstation and they gained four times of improvement.…”

Section: Ieee Congress On Evolutionary Computationmentioning

confidence: 99%

“…1-A means of implementing the fitness functions of the GA or genetic programming (GP) [17]. 2-A platform for implementing the EAs (especially for GA) like the GA processor (GAP) [7], [18], [19] for general optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…Koza et al [17]used an FPGA to speed up the fitness evaluation of a sorting network, in which the FPGA was used solely to perform the fitness evaluation. The initial population was created by a host computer, and then individuals were downloaded to the pre-programmed FPGA and the FPGA was instructed to evaluate the fitness of the individual.…”

Section: Introductionmentioning

confidence: 99%

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SOPC-Based Parallel Genetic Algorithm

Jelodar

Kamal²,

Fakhraie³

et al.

2006 IEEE International Conference on Evolutionary Computation

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The ever-growing complexity of the modern chips is forcing fundamental changes in the way systems are designed. System-on-a-Programmable-Chip (SOPC) concept is bringing a major revolution in the design of integrated circuits, due to the fact that it makes unprecedented levels of in-field integration possible. Genetic Algorithm (GA) is a powerful function optimizer that is used successfully to solve problems in many different disciplines. A major drawback of GA is that it needs huge computation time for sequential execution on PCs. Therefore, the hardware implementation of GA has been the focus of some recent studies. Parallel GA (PGA) is particularly important for efficient hardware implementation and promise substantial gains in performance and results. In this paper, a SOPC-based PGA framework is proposed. Our proposed framework can be used in real-time applications. We have implemented our proposed system on an Altera ® Stratix Development Kit and we compare its performance with the corresponding software simulation. The results obtained indicate a speedup of up to 50 times in the elapsed computation time.

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Evolving sorting networks using genetic programming and the rapidly reconfigurable Xilinx 6216 field-programmable gate array

Cited by 15 publications

References 4 publications

Sorting networks: To the end and back again

Sorting networks: To the end and back again

Automatic Algorithm Development Using New Reinforcement Programming Techniques

SOPC-Based Parallel Genetic Algorithm

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