@misc{langdon2010talk,
author = {William B. Langdon},
title = {Programming Graphics Cards with CUDA for Genetic Programming},
howpublished = {Ecole d'été Evolution Artificielle 2010},
year = "2010",
month = {14-17 June},
note = {Invited talk},
address = {Calais},
keywords = {genetic algorithms, genetic programming},
url = {http://sites.google.com/site/ecoleea2010/supports-de-cours-et-tps},
size = {39 pages}
)
@InProceedings{Maitre:2010:EuroGP,
author = "Ogier Maitre and Pierre Collet and Nicolas Lachiche",
title = "Fast Evaluation of {GP} Trees on {GPGPU} by Optimizing Hardware Scheduling",
booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "301--312",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_26"
}
@InProceedings{KMJBC10,
author = {F. Krüger and O. Maitre, S and Jimenez and L. Baumes and P. Collet},
title = {Speedups between x70 and x120 for a generic local search (memetic) algorithm on a single GPGPU chip},
booktitle = {EvoNum 2010},
series = "LNCS",
year = {2010},
publisher = {Springer},
volume = "6024",
url = {http://lsiit-cnrs.unistra.fr/Publications/2010/KMJBC10},
pages = "501-511"
}
@INPROCEEDINGS{9184,
author = {Petr Pospíchal and Jiří Jaroš and Josef Schwarz},
title = {Parallel Genetic Algorithm on the CUDA Architecture},
pages = {442--451},
booktitle = {Applications of Evolutionary Computation},
series = {LNCS 6024},
year = {2010},
location = {Berlin Heidelberg, DE},
publisher = {Springer Verlag},
ISBN = {978-3-642-12238-5},
language = {english},
url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9184}
}
@INPROCEEDINGS{9253,
author = {Petr Pospíchal and Josef Schwarz and Jiří Jaroš},
title = {Parallel Genetic Algorithm Solving 0/1 Knapsack Problem Running on the GPU},
pages = {64--70},
booktitle = {16th International Conference on Soft Computing MENDEL 2010},
year = {2010},
location = {Brno, CZ},
publisher = {Brno University of Technology},
ISBN = {978-80-214-4120-0},
language = {english},
url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9253}
}
@inproceedings{chitty_data_2007,
address = {London, England},
title = {A data parallel approach to genetic programming using programmable graphics hardware},
isbn = {978-1-59593-697-4},
url = {http://portal.acm.org/citation.cfm?id=1276958.1277274},
doi = {10.1145/1276958.1277274},
abstract = {In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community aslow cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards.},
booktitle = {Proceedings of the 9th annual conference on Genetic and evolutionary computation},
publisher = {{ACM}},
author = {Darren M. Chitty},
year = {2007},
keywords = {data parallelism, genetic programming, graphics cards},
pages = {1566--1573}
},
@book{poli_field_2008,
title = {A Field Guide to Genetic Programming},
isbn = {1409200736, 9781409200734},
publisher = {Lulu.com},
author = {Riccardo Poli and W B Langdon and Nicholas Freitag {McPhee}},
month = mar,
year = {2008}
},
@incollection{langdon_simd_2008,
title = {A {SIMD} Interpreter for Genetic Programming on {GPU} Graphics Cards},
url = {http://dx.doi.org/10.1007/978-3-540-78671-9_7},
abstract = {{Mackey-Glass} chaotic time series prediction and nuclear protein classification show the feasibility of evaluating genetic
programming populations directly on parallel consumer gaming graphics processing units. Using a Linux {KDE} computer equipped with an {nVidia} {GeForce} 8800 {GTX}
graphics processing unit card the C++ {SPMD} interpretter evolves programs at Giga {GP} operations per second (895 million {GPops).}
We use the {RapidMind} general processing on {GPU} {(GPGPU)} framework to evaluate an entire population of a quarter of a million
individual programs on a non-trivial problem in 4 seconds. An efficient reverse polish notation {(RPN)} tree based {GP} is given.},
booktitle = {Genetic Programming},
author = {W. Langdon and Wolfgang Banzhaf},
year = {2008},
pages = {73--85}
},
@incollection{banzhaf_accelerating_2009,
title = {Accelerating Genetic Programming through Graphics Processing Units.},
url = {http://dx.doi.org/10.1007/978-0-387-87623-8_15},
booktitle = {Genetic Programming Theory and Practice {VI}},
author = {Wolfgang Banzhaf and Simon Harding and William B. Langdon and Garnett Wilson},
year = {2009},
pages = {1--19}
},
@inproceedings{jian-ming_li_efficient_2007,
title = {An Efficient Fine-grained Parallel Genetic Algorithm Based on {GPU-Accelerated}},
doi = {{10.1109/NPC.2007.108}},
abstract = {Fine-grained parallel genetic algorithm {(FGPGA),} though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a {FGPGA} method based on {GPU-acceleration,} which maps parallel {GA} algorithm to texture-rendering on consumer-level graphics cards. The analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible {FGPGA} solution.},
booktitle = {Network and Parallel Computing Workshops, 2007. {NPC} Workshops. {IFIP} International Conference on},
author = {{Jian-Ming} Li and {Xiao-Jing} Wang and {Rong-Sheng} He and {Zhong-Xian} Chi},
year = {2007},
keywords = {consumer-level graphics cards, data communication, efficient fine-grained parallel genetic algorithm, genetic algorithms, {GPU-acceleration,} optimization problems, parallel computers, rendering (computer graphics), texture-rendering},
pages = {855--862}
},
@inproceedings{changhao_jiang_automatic_2005,
title = {Automatic tuning matrix multiplication performance on graphics hardware},
isbn = {{1089-795X}},
doi = {{10.1109/PACT.2005.10}},
abstract = {In order to utilize the tremendous computing power of graphics hardware and to automatically adapt to the fast and frequent changes in its architecture and performance characteristics, this paper implements an automatic tuning system to generate high-performance matrix-multiplication implementation on graphics hardware. The automatic tuning system uses a parameterized code generator to generate multiple versions of matrix multiplication, whose performances are empirically evaluated by actual execution on the target platform. An ad-hoc search engine is employed to search over the implementation space for the version that yields the best performance. In contrast to similar systems on {CPUs,} which utilize cache blocking, register tiling, instruction scheduling tuning strategies, this paper identifies and exploits several tuning strategies that are unique for graphics hardware. These tuning strategies include optimizing for multiple-render-targets, {SIMD} instructions with data packing, overcoming limitations on instruction count and dynamic branch instruction. The generated implementations have comparable performance with expert manually tuned version in spite of the significant overhead incurred due to the use of the high-level {BrookGPU} language.},
booktitle = {Parallel Architectures and Compilation Techniques, 2005. {PACT} 2005. 14th International Conference on},
author = {Changhao Jiang and M. Snir},
year = {2005},
keywords = {ad-hoc search engine, automatic tuning matrix multiplication, cache blocking, computer graphic equipment, coprocessors, data packing, dynamic branch instruction, graphics hardware, instruction scheduling tuning, matrix multiplication, multiple-render-targets, parameterized code generator, register tiling, {SIMD} instructions},
pages = {185--194}
},
@inproceedings{miller_cartesian_2008,
address = {Atlanta, {GA,} {USA}},
title = {Cartesian genetic programming},
isbn = {978-1-60558-131-6},
url = {http://portal.acm.org/citation.cfm?id=1388969.1389075},
doi = {10.1145/1388969.1389075},
abstract = {Note: {OCR} errors may be found in this Reference List extracted from the full text article. {ACM} has opted to expose the complete List rather than only correct and linked references.},
booktitle = {Proceedings of the 2008 {GECCO} conference companion on Genetic and evolutionary computation},
publisher = {{ACM}},
author = {Julian Francis Miller and Simon L. Harding},
year = {2008},
keywords = {genetic programming},
pages = {2701--2726}
},
@book{alba_cellular_2008,
title = {Cellular genetic algorithms},
isbn = {0387776095, 9780387776095},
publisher = {Springer},
author = {Enrique Alba and Bernabe Dorronsoro},
year = {2008}
},
@inproceedings{zhongwen_luo_cellular_2006,
title = {Cellular Genetic Algorithms and Local Search for {3-SAT} problem on Graphic Hardware},
doi = {{10.1109/CEC.2006.1688685}},
abstract = {As a well known {NP-hard} problem, {SAT} problem is widely discussed by computer science society. In this paper, two common algorithms for {SAT} problems are implemented based on graphic hardware. They are greedy local search and genetic algorithm. After a brief description of the basic algorithm, we give our modification of the algorithm for fitting with graphic hardware's {SIMD} model. Then some implementation details and tricks for graphic hardware are given. At last testing result are given. We compared the performance of graphic hardware based computation with {CPU} based computation. The result shows a performance improvement on graphic hardware over ordinary {CPU.}},
booktitle = {Evolutionary Computation, 2006. {CEC} 2006. {IEEE} Congress on},
author = {Zhongwen Luo and Hongzhi Liu},
year = {2006},
keywords = {{3-SAT} problem, cellular genetic algorithms, computability, computational complexity, computer graphic equipment, convergence, genetic algorithms, graphic hardware {SIMD} model, greedy algorithms, greedy local search, mathematics computing, {NP-hard} problem, parallel algorithms, search problems},
pages = {2988--2992}
},
@article{fok_evolutionary_2007,
title = {Evolutionary Computing on Consumer Graphics Hardware},
volume = {22},
issn = {1541-1672},
doi = {{10.1109/MIS.2007.28}},
abstract = {We propose implementing a parallel {EA} on consumer graphics cards, which we can find in many {PCs.} This lets more people use our parallel algorithm to solve large-scale, real-world problems such as data mining. Parallel evolutionary algorithms run on consumer-grade graphics hardware achieve better execution times than ordinary evolutionary algorithms and offer greater accessibility than those run on high-performance computers},
number = {2},
journal = {Intelligent Systems, {IEEE}},
author = {{Ka-Ling} Fok and {Tien-Tsin} Wong},
year = {2007},
keywords = {computer graphic equipment, computer graphics, consumer graphics card, consumer-grade graphics hardware, evolutionary algorithms, evolutionary computation, evolutionary computing, high-performance computer, parallel algorithm, parallel algorithms, parallel evolutionary algorithm, pervasive computing, scientific computing on graphics-processing units, ubiquitous computing},
pages = {69--78}
},
@inproceedings{harding_fast_2007,
title = {Fast Genetic Programming and Artificial Developmental Systems on {GPUs}},
isbn = {0-7695-2813-9},
url = {http://portal.acm.org/citation.cfm?id=1256286},
abstract = {In this paper we demonstrate the use of the Graphics Processing Unit {(GPU)} to accelerate Evolutionary Computation applications, in particular Genetic Programming approaches. We show that it is possible to get speed increases of several hundred times over a typical {CPU} implementation, catapulting {GPU} processing for these applications into the realm of {HPC.} This increase in performance also extends to artificial developmental systems, where evolved programs are used to construct cellular systems. Feasibility of this approach to efficiently evaluate artificial developmental systems based on cellular automata is demonstrated.},
booktitle = {Proceedings of the 21st International Symposium on High Performance Computing Systems and Applications},
publisher = {{IEEE} Computer Society},
author = {Simon Harding and Wolfgang Banzhaf},
year = {2007},
pages = {2}
},
@incollection{harding_fast_2007-1,
title = {Fast Genetic Programming on {GPUs}},
url = {http://dx.doi.org/10.1007/978-3-540-71605-1_9},
abstract = {As is typical in evolutionary algorithms, fitness evaluation in {GP} takes the majority of the computational effort. In this paper we demonstrate the use of the Graphics Processing Unit {(GPU)} to accelerate the evaluation of individuals. We show that for both binary and floating point based data types, it is possible to get speed increases of several hundred times over a typical {CPU} implementation. This allows for evaluation of many thousands of fitness cases, and hence should enable more ambitious solutions to be evolved using {GP.}},
booktitle = {Genetic Programming},
author = {Simon Harding and Wolfgang Banzhaf},
year = {2007},
pages = {90--101}
},
@article{poli_genetic_2007,
title = {Genetic programming an introductory tutorial and a survey of techniques and applications},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.3889},
author = {Riccardo Poli and William B Langdon and Nicholas F Mcphee and John R Koza},
year = {2007}
},
@article{harding_genetic_2008,
title = {Genetic programming on {GPUs} for image processing},
volume = {1},
url = {http://www.inderscience.com/search/index.php?action=record&rec_id=24207&prevQuery=&ps=10&m=or},
doi = {{10.1504/IJHPSA.2008.024207}},
abstract = {The evolution of image filters using genetic programming is a relatively unexplored task. This is most likely due to the high computational cost of evaluating the evolved programs. The parallel processors available on modern graphics cards can be used to greatly increase the speed of evaluation. Previous papers in this area dealt with tasks such as noise reduction and edge detection. Here we demonstrate that other more complicated processes can also be successfully evolved and that we can 'reverse engineer' the output from filters used in common graphics manipulation programs.},
number = {4},
journal = {International Journal of High Performance Systems Architecture},
author = {S. Harding and W. Banzhaf},
year = {2008},
keywords = {genetic programming, {GPU,} graphics processing units, image filters, image processing, parallel processing, reverse engineering},
pages = {231 -- 240}
},
@article{langdon_gpspmd_2008,
title = {{GP} on {SPMD} parallel graphics hardware for mega Bioinformatics data mining},
volume = {12},
url = {http://dx.doi.org/10.1007/s00500-008-0296-x},
doi = {10.1007/s00500-008-0296-x},
abstract = {Abstract We demonstrate a {SIMD} C++ genetic programming system on a single 128 node parallel {nVidia} {GeForce} 8800 {GTX} {GPU} under {RapidMind’s}
{GPGPU} Linux software by predicting ten year+ outcome of breast cancer from a dataset containing a million inputs. {NCBI} {GEO}
{GSE3494} contains hundreds of Affymetrix {HG-U133A} and {HG-U133B} {GeneChip} biopsies. Multiple {GP} runs each with a population of
5 million programs winnow useful variables from the chaff at more than 500 million {GPops} per second. Sources available via
{FTP.}},
number = {12},
journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications},
author = {W. Langdon and A. Harrison},
month = oct,
year = {2008},
pages = {1169--1183}
},
@incollection{gobron_gpu_2008,
title = {{GPU} Accelerated Computation and Visualization of Hexagonal Cellular Automata},
url = {http://dx.doi.org/10.1007/978-3-540-79992-4_67},
abstract = {We propose a graphics processor unit {(GPU)-accelerated} method for real-time computing and rendering cellular automata {(CA)}
that is applied to hexagonal {grids.Based} on our previous work [9] –which introduced first and second dimensional cases– this
paper presents a model for hexagonal grid algorithms. Proposed method is novel and it encodes and transmits large {CA} key-codes
to the graphics card and consequently, this technique allows to visualize the {CA} information flow in real-time to easily identify
emerging behaviors even for large data sets. To show the efficiency of our model we first present a set of characteristic
hexagonal behaviors, and then describe computational statistics for central processing unit {(CPU)} and {GPU} on a set of different
hardware and operating system {(OS)} configurations. We show that our model is flexible and very efficient as it permits to
compute {CA} close to a thousand times faster than classical {CPU} methods. Additionally, free access is provided to our downloadable
software for hexagonal grid {CA} simulations.},
booktitle = {Cellular Automata},
author = {Stephane Gobron and Herva Bonafos and Daniel Mestre},
year = {2008},
pages = {512--521}
},
@incollection{vasicek_hardware_2008,
title = {Hardware Accelerators for Cartesian Genetic Programming},
url = {http://dx.doi.org/10.1007/978-3-540-78671-9_20},
abstract = {A new class of {FPGA-based} accelerators is presented for Cartesian Genetic Programming {(CGP).} The accelerators contain a genetic
engine which is reused in all applications. Candidate programs (circuits) are evaluated using application-specific virtual
reconfigurable circuit {(VRC)} and fitness unit. Two types of {VRCs} are proposed. The first one is devoted for symbolic regression
problems over the fixed point representation. The second one is designed for evolution of logic circuits. In both cases a
significant speedup of evolution (30–40 times) was obtained in comparison with a highly optimized software implementation
of {CGP.} This speedup can be increased by creating multiple fitness units.},
booktitle = {Genetic Programming},
author = {Zdenek Vasicek and Lukas Sekanina},
year = {2008},
pages = {230--241}
},
@inproceedings{robilliard_high_2009,
address = {Barcelona, Spain},
title = {High performance genetic programming on {GPU}},
isbn = {978-1-60558-584-0},
url = {http://portal.acm.org/citation.cfm?id=1555284.1555299},
doi = {10.1145/1555284.1555299},
abstract = {The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming {(GP)} on Graphics Processing Units {(GPUs).} Our work focuses on the possibilities offered by Nvidia G80 {GPUs} when programmed in the {CUDA} language. We compare two parallelization schemes that evaluate several {GP} programs in parallel. We show that the fine grain distribution of computations over the elementary processors greatly impacts performances. We also present memory and representation optimizations that further enhance computation speed, up to 2.8 billion {GP} operations per second. The code has been developed with the well known {ECJ} library.},
booktitle = {Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems},
publisher = {{ACM}},
author = {Denis Robilliard and Virginie Marion and Cyril Fonlupt},
year = {2009},
keywords = {genetic algorithms, genetic programming, graphics processing units, parallel processing},
pages = {85--94}
},
@incollection{wong_implementation_2009,
title = {Implementation of Parallel Genetic Algorithms on Graphics Processing Units},
url = {http://dx.doi.org/10.1007/978-3-540-95978-6_14},
abstract = {In this paper, we propose to parallelize a Hybrid Genetic Algorithm {(HGA)} on Graphics Processing Units {(GPUs)} which are available
and installed on ubiquitous personal computers. {HGA} extends the classical genetic algorithm by incorporating the Cauchy mutation
operator from evolutionary programming. In our parallel {HGA,} all steps except the random number generation procedure are performed
in {GPU} and thus our parallel {HGA} can be executed effectively and efficiently. We suggest and develop the novel pseudo-deterministic
selection method which is comparable to the traditional global selection approach with significant execution time performance
{advantages.We} perform experiments to compare our parallel {HGA} with our previous parallel {FEP} {(Fast} Evolutionary programming)
and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations
of {HGA} are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness
and efficiency of the pseudo-deterministic selection method is also studied.},
booktitle = {Intelligent and Evolutionary Systems},
author = {Man Wong and Tien Wong},
year = {2009},
pages = {197--216}
},
@inproceedings{wilson_linear_2008,
title = {Linear genetic programming {GPGPU} on Microsoft's Xbox 360},
doi = {{10.1109/CEC.2008.4630825}},
abstract = {We describe how to harness the graphics processing abilities of a consumer video game console {(Xbox} 360) for general programming on graphics processing unit {(GPGPU)} purposes. In particular, we implement a linear {GP} {(LGP)} system to solve classification and regression problems. We conduct inter- and intra-platform benchmarking of the Xbox 360 and {PC,} using {GPU} and {CPU} implementations on both architectures. Platform benchmarking confirms highly integrated {CPU} and {GPU} programming flexibility of the Xbox 360, having the potential to alleviate typical {GPGPU} decisions of allocating particular functionalities to {CPU} or {GPU.}},
booktitle = {Evolutionary Computation, 2008. {CEC} 2008. {(IEEE} World Congress on Computational Intelligence). {IEEE} Congress on},
author = {G. Wilson and W. Banzhaf},
year = {2008},
keywords = {classification problems, computer graphic equipment, computer graphics, consumer video game console, genetic algorithms, graphics processing abilities, graphics processing unit, linear genetic programming, linear programming, Microsoft Xbox 360, regression analysis, regression problems},
pages = {378--385}
},
@incollection{wong_parallel_2006,
title = {Parallel Evolutionary Algorithms on {Consumer-Level} Graphics Processing Unit},
url = {http://dx.doi.org/10.1007/3-540-32839-4_7},
abstract = {Evolutionary Algorithms {(EAs)} are effective and robust methods for solving many practical problems such as feature selection,
electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because
several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms.
In this chapter, we propose to implement a parallel {EA} on consumer-level Graphics Processing Unit {(GPU).} We perform experiments to compare our parallel {EA} with an ordinary {EA} and demonstrate that the former is much more
effective than the latter. Since consumer-level graphics processing units are already widely available and installed on oridinary
personal computers and they are easy to use and manage, more people will be able to use our parallel algorithm to solve their
problems encountered in real-world applications.},
booktitle = {Parallel Evolutionary Computations},
author = {{Tien-Tsin} Wong and Man Wong},
year = {2006},
pages = {133--155}
},
@inproceedings{man-leung_wong_parallel_2005,
title = {Parallel evolutionary algorithms on graphics processing unit},
volume = {3},
doi = {{10.1109/CEC.2005.1554979}},
abstract = {Evolutionary algorithms {(EAs)} are effective and robust methods for solving many practical problems such as feature selection, electrical circuit synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose to implement a parallel {EA} on consumer-level graphics cards. We perform experiments to compare our parallel {EA} with an ordinary {EA} and demonstrate that the former is much more effective than the latter. Since consumer-level graphics cards are available in ubiquitous personal computers and these computers are easy to use and manage, more people are able to use our parallel algorithm to solve their problems encountered in real-world applications.},
booktitle = {Evolutionary Computation, 2005. The 2005 {IEEE} Congress on},
author = {{Man-Leung} Wong and {Tien-Tsin} Wong and {Ka-Ling} Fok},
year = {2005},
keywords = {computer graphic equipment, evolutionary computation, fitness evaluations, graphics cards, graphics processing unit, parallel algorithm, parallel algorithms, parallel evolutionary algorithms, ubiquitous computing, ubiquitous personal computers},
pages = {2286--2293 Vol. 3}
},
@incollection{yu_parallel_2005,
title = {Parallel Genetic Algorithms on Programmable Graphics Hardware},
url = {http://dx.doi.org/10.1007/11539902_134},
abstract = {Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. This paper describes how fine-grained parallel genetic algorithms can be mapped to programmable graphics hardware found in commodity {PC.} Our approach stores chromosomes and their fitness values in texture memory on graphics card. Both fitness evaluation and genetic operations are implemented entirely with fragment programs executed on graphics processing unit in parallel. We demonstrate the effectiveness of our approach by comparing it with compatible software implementation. The presented approach allows us benefit from the advantages of parallel genetic algorithms on low-cost platform.},
booktitle = {Advances in Natural Computation},
author = {Qizhi Yu and Chongcheng Chen and Zhigeng Pan},
year = {2005},
pages = {1051--1059}
},
@inproceedings{man-leung_wong_parallel_2006,
title = {Parallel Hybrid Genetic Algorithms on {Consumer-Level} Graphics Hardware},
doi = {{10.1109/CEC.2006.1688683}},
abstract = {In this paper, we report a parallel hybrid genetic algorithm {(HGA)} on consumer-level graphics cards. {HGA} extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel {HGA,} all steps except the random number generation procedure are performed in graphics processing unit {(GPU)} and thus our parallel {HGA} can be executed effectively and efficiently. We propose the pseudo-deterministic selection method which is comparable to the traditional global selection approach with significant execution time performance advantages. We perform experiments to compare our parallel {HGA} with our previous parallel {FEP} (fast evolutionary programming) and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations of {HGA} are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness and efficiency of the pseudo-deterministic selection method is also studied.},
booktitle = {Evolutionary Computation, 2006. {CEC} 2006. {IEEE} Congress on},
author = {{Man-Leung} Wong and {Tien-Tsin} Wong},
year = {2006},
keywords = {Cauchy mutation operator, computer graphic equipment, consumer-level graphics hardware, evolutionary programming, genetic algorithms, graphics processing unit, parallel algorithms, parallel hybrid genetic algorithm, pseudo-deterministic selection method, rendering (computer graphics)},
pages = {2973--2980}
},
@incollection{robilliard_population_2008,
title = {Population Parallel {GP} on the G80 {GPU}},
url = {http://dx.doi.org/10.1007/978-3-540-78671-9_9},
abstract = {The availability of low cost powerful parallel graphics cards has stimulated a trend to port {GP} on Graphics Processing Units
{(GPUs).} Previous works on {GPUs} have shown evaluation phase speedups for large training cases sets. Using the {CUDA} language
on the G80 {GPU,} we show it is possible to efficiently interpret several {GP} programs in parallel, thus obtaining speedups also
for small training sets starting at less than 100 training cases. Our scheme was embedded in the well-known {ECJ} library, providing
an easy entry point for owners of G80 {GPUs.}},
booktitle = {Genetic Programming},
author = {Denis Robilliard and Virginie {Marion-Poty} and Cyril Fonlupt},
year = {2008},
pages = {98--109}
}
@inproceedings{DBLP:conf/gecco/Zhu09,
author = {Weihang Zhu},
title = {A study of parallel evolution strategy: pattern search on
a GPU computing platform},
booktitle = {GEC Summit},
year = {2009},
pages = {765-772},
ee = {http://doi.acm.org/10.1145/1543834.1543939},
editor = {Lihong Xu and
Erik D. Goodman and
Guoliang Chen and
Darrell Whitley and
Yongsheng Ding},
title = {Genetic and Evolutionary Computation Conference, GEC Summit
2009, Proceedings, Shanghai, China, June 12-14, 2009},
booktitle = {GEC Summit},
publisher = {ACM},
year = {2009},
isbn = {978-1-60558-326-6},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
@inproceedings{1570355,
author = {Tsutsui, Shigeyoshi and Fujimoto, Noriyuki},
title = {Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2523--2530},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570355},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570356,
author = {Wilson, Garnett and Banzhaf, Wolfgang},
title = {Deployment of CPU and GPU-based genetic programming on heterogeneous devices},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2531--2538},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570356},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570419,
author = {Banzhaf, Wolfgang and Harding, Simon},
title = {Accelerating evolutionary computation with graphics processing units},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {3237--3286},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570419},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570352,
author = {Cavanagh, Joseph M. and Potok, Thomas E. and Cui, Xiaohui},
title = {Parallel latent semantic analysis using a graphics processing unit},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2505--2510},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570352},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570353,
author = {Langdon, W. B.},
title = {A fast high quality pseudo random number generator for nVidia CUDA},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2511--2514},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570353},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570354,
author = {Wong, Man Leung},
title = {Parallel multi-objective evolutionary algorithms on graphics processing units},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2515--2522},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570354},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570350,
author = {Perez-Miguel, Carlos and Miguel-Alonso, Jose and Mendiburu, Alexander},
title = {Evaluating the cell broadband engine as a platform to run estimation of distribution algorithms},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2491--2498},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570350},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{1570351,
author = {Rieffel, John and Saunders, Frank and Nadimpalli, Shilpa and Zhou, Harvey and Hassoun, Soha and Rife, Jason and Trimmer, Barry},
title = {Evolving soft robotic locomotion in PhysX},
booktitle = {GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference},
year = {2009},
isbn = {978-1-60558-505-5},
pages = {2499--2504},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1570256.1570351},
publisher = {ACM},
address = {New York, NY, USA},
}
@TechReport{langdon:2009:TR-09-05,
author = "W. B. Langdon",
title = "A {CUDA} {SIMT} Interpreter for Genetic~Programming",
institution = "Department of Computer Science, King's College London",
year = "2009",
number = "TR-09-05",
address = "Strand, WC2R 2LS, UK",
month = "18 " # jun,
note = "Revised",
keywords = "genetic algorithms, genetic programming, GPU, GPGPU,
Tesla, sub-machine code GP, CUDA",
URL = "http://www.dcs.kcl.ac.uk/technical-reports/papers/TR-09-05.pdf",
URL2 = "http://www.gpgpgpu.com/gecco2009/5.pdf",
abstract = "A Single Instruction Multiple Thread CUDA interpreter
provides SIMD like parallel evaluation of the whole GP
population of quarter of a million RPN expressions on
graphics cards and nVidia Tesla T10P. Using sub-machine
code GP a sustain peak performance of 212 billion GP
operations per second (3300 speed up) and an average of
4.5 peta GP ops per day is reported for a single card
on a Boolean induction benchmark never attempted
before, let alone solved.",
size = "2 pages",
}
@inproceedings{1543939,
author = {Zhu, Weihang},
title = {A study of parallel evolution strategy: pattern search on a GPU computing platform},
booktitle = {GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation},
year = {2009},
isbn = {978-1-60558-326-6},
pages = {765--772},
location = {Shanghai, China},
doi = {http://doi.acm.org/10.1145/1543834.1543939},
publisher = {ACM},
address = {New York, NY, USA},
}
@incollection{zhu_multi-walk_2009,
title = {Multi-walk Parallel Pattern Search Approach on a {GPU} Computing Platform},
url = {http://dx.doi.org/10.1007/978-3-642-01970-8_99},
abstract = {This paper studies the efficiency of using Pattern Search {(PS)} on bound constrained optimization functions on a Graphics Processing
Unit {(GPU)} computing platform. Pattern Search is a direct search optimization technique that does not require derivative information
on non-linear programming problems. Pattern Search is ideally suited to a {GPU} computing environment due to its low memory
requirement and no communication between threads in a multi-walk setting. To adapt to a {GPU} environment, traditional Pattern
Search is modified by terminating based on iterations instead of tolerance. This research designed and implemented a multi-walk
Pattern Search algorithm on a {GPU} computing platform. Computational results are promising with a computing speedup of 100+
compared to a corresponding implementation on a single {CPU.}},
booktitle = {Computational Science – {ICCS} 2009},
author = {Weihang Zhu and James Curry},
year = {2009},
pages = {984--993}
}
@inproceedings{1570089,
author = {Maitre, Ogier and Baumes, Laurent A. and Lachiche, Nicolas and Corma, Avelino and Collet,Pierre},
title = {Coarse grain parallelization of evolutionary algorithms on {GPGPU} cards with {EASEA}},
booktitle = {GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation},
year = {2009},
isbn = {978-1-60558-325-9},
pages = {1403--1410},
location = {Montreal, Qu\'{e}bec, Canada},
doi = {http://doi.acm.org/10.1145/1569901.1570089},
url = {http://portal.acm.org/citation.cfm?id=1570089},
publisher = {ACM},
address = {New York, NY, USA},
}
@INPROCEEDINGS{Diaz2009,
author = {Josefa Díaz and Francisco Fernández de Vega and J. Ignacio Hidalgo
and Oscar Garnica and Sonia López},
title = {Applying Genetic Algorithms to Resizable Caches Configuration for
Improving SMT Performance},
booktitle = {WPABA'09: Proceedings of the Second International Workshop on Parallel
Architectures and Bioinspired Algorithms (WPABA 2009)},
year = {2009},
editor = {José L. Risco-Martín and Oscar Garnica},
pages = {39-48},
address = {Raleigh, NC, USA},
month = {September 12-16},
publisher = {Universidad Complutense de Madrid},
owner = {jlrisco},
timestamp = {2009.09.30}
}
@INPROCEEDINGS{Harding2009,
author = {Simon Harding and Wolfgang Banzhaf},
title = {Distributed Genetic Programming on GPUs using CUDA},
booktitle = {WPABA'09: Proceedings of the Second International Workshop on Parallel
Architectures and Bioinspired Algorithms (WPABA 2009)},
year = {2009},
editor = {José L. Risco-Martín and Oscar Garnica},
pages = {1-10},
address = {Raleigh, NC, USA},
month = {September 12-16},
publisher = {Universidad Complutense de Madrid},
owner = {jlrisco},
timestamp = {2009.09.30},
URL = "http://www.evolutioninmaterio.com/preprints/CudaParallelCompilePP.pdf",
abstract = "Using of a cluster of Graphics Processing Unit (GPU)
equipped computers, it is possible to accelerate the
evaluation of individuals in Genetic Programming.
Program compilation, fitness case data and fitness
execution are spread over the cluster of computers,
allowing for the efficient processing of very large
datasets. Here, the implementation is demonstrated on
datasets containing over 10 million rows and several
hundred megabytes in size.
Populations of candidate individuals are compiled into
NVidia CUDA programs and executed on a set of client
computers - each with a different subset of the
dataset.
The paper discusses the implementation of the system
and acts as a tutorial for other researchers
experimenting with genetic programming and GPUs.",
}
@INPROCEEDINGS{Napoli2009,
author = {Claudia Di Napoli and Maurizio Giordano and Zsolt Németh and Nicola
Tonellotto},
title = {A Chemical Metaphor to Model Service Selection for Composition of
Services},
booktitle = {WPABA'09: Proceedings of the Second International Workshop on Parallel
Architectures and Bioinspired Algorithms (WPABA 2009)},
year = {2009},
editor = {José L. Risco-Martín and Oscar Garnica},
pages = {11-20},
address = {Raleigh, NC, USA},
month = {September 12-16},
publisher = {Universidad Complutense de Madrid},
owner = {jlrisco},
timestamp = {2009.09.30}
}
@INPROCEEDINGS{PerezMiguel2009,
author = {Carlos Pérez-Miguel and José Miguel-Alonso and Alexander Mendiburu},
title = {Porting Estimation of Distribution Algorithms to the Cell Broadband
Engine},
booktitle = {WPABA'09: Proceedings of the Second International Workshop on Parallel
Architectures and Bioinspired Algorithms (WPABA 2009)},
year = {2009},
editor = {José L. Risco-Martín and Oscar Garnica},
pages = {31-38},
address = {Raleigh, NC, USA},
month = {September 12-16},
publisher = {Universidad Complutense de Madrid},
owner = {jlrisco},
timestamp = {2009.09.30}
}
@INPROCEEDINGS{RiscoMartin2009,
author = {José L. Risco-Martín and José M. Colmenar and Rubén Gonzalo},
title = {A Parallel Evolutionary Algorithm to Optimize Dynamic Memory Managers
in Embedded Systems},
booktitle = {WPABA'09: Proceedings of the Second International Workshop on Parallel
Architectures and Bioinspired Algorithms (WPABA 2009)},
year = {2009},
editor = {José L. Risco-Martín and Oscar Garnica},
pages = {21-30},
address = {Raleigh, NC, USA},
month = {September 12-16},
publisher = {Universidad Complutense de Madrid},
owner = {jlrisco},
timestamp = {2009.09.30}
}
@InProceedings{DBLP:conf/gecco/LewisM09,
author = "Tony E. Lewis and George D. Magoulas",
title = "Strategies to minimise the total run time of cyclic
graph based genetic programming with {GPU}s",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "Guenther Raidl and Franz Rothlauf and
Giovanni Squillero and Rolf Drechsler and others",
pages = "1379--1386",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, cyclic
cartesian genetic programming, GPU, deme, parallel
interpretter",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://doi.acm.org/10.1145/1569901.1570086",
size = "8 pages",
}
@inproceedings{DBLP:conf/ausai/XuCKLV09,
author = {Yanyan Xu and Hui Chen and Reinhard Klette and Jiaju Liu and Tobi Vaudrey},
title = {Belief Propagation Implementation Using CUDA on an NVIDIA GTX 280},
booktitle = {Australasian Conference on Artificial Intelligence},
year = {2009},
pages = {180-189},
URL = {http://dx.doi.org/10.1007/978-3-642-10439-8_19},
bibsource = {DBLP, http://dblp.uni-trier.de},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
editor = {Ann E. Nicholson and Xiaodong Li}
}
@InCollection{langdon:2009:pdci,
author = "W. B. Langdon",
title = "Large Scale Bioinformatics Data Mining with Parallel
Genetic Programming on Graphics Processing Units",
booktitle = "Parallel and Distributed Computational Intelligence",
publisher = "Springer",
year = "2010",
editor = "Francisco Fernandez {de Vega} and Erick Cantu-Paz",
volume = "279",
series = "Studies in Computational Intelligence",
chapter = "5",
pages = "113--141",
month = jan,
keywords = "genetic algorithms, genetic programming, GPU",
isbn13 = "978-3642106743",
URL = "http://www.springer.com/engineering/book/978-3-642-10674-3",
doi = "doi:10.1007/978-3-642-10675-0_6",
abstract = "A suitable single instruction multiple data GP
interpreter can achieve high (Giga GPop/second)
performance on a SIMD GPU graphics card by
simultaneously running multiple diverse members of the
genetic programming population. SPMD dataflow
parallelisation is achieved because the single
interpreter treats the different GP programs as data.
On a single 128 node parallel nVidia GeForce 8800 GTX
GPU, the interpreter can out run a compiled approach,
where data parallelisation comes only by running a
single program at a time across multiple inputs.
The RapidMind GPGPU Linux C++ system has been
demonstrated by predicting ten year+ outcome of breast
cancer from a dataset containing a million inputs. NCBI
GEO GSE3494 contains hundreds of Affymetrix
\mbox{HG-U133A} and HG-U133B GeneChip biopsies.
Multiple GP runs each with a population of five million
programs winnow useful variables from the chaff at more
than 500 million GPops per second. Sources available
via
\href{http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gpu_gp_2.tar.gz}
{FTP}.",
size = "28 pages",
}
@InProceedings{langdon:2010:eurogp,
author = "W. B. Langdon",
title = "A Many Threaded {CUDA} Interpreter for Genetic
Programming",
booktitle = "EuroGP 2010",
year = "2010",
editor = "Anna I Esparcia-Alcazar and Aniko Ekart and
Sara Silva",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, GPU, GPGPU,
Tesla, sub-machine code GP, Reverse Polish Notation",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2010_eurogp.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2010_eurogp.ps.gz",
size = "12 pages",
abstract = "A Single Instruction Multiple Thread CUDA interpreter
provides SIMD like parallel evaluation of the whole GP
population of 0.25 million trees reverse polish
notation (RPN) expressions on graphics cards and nVidia
Tesla T10P. Using sub-machine code GP a sustain peak
performance of 215 billion GP operations per second
(3400 speed up) and an average of 10 peta GP ops per
day is reported for a single GPU card on a Boolean
induction benchmark never attempted before, let alone
solved.",
}
@Article{Robilliard:2009:GPEM,
author = "Denis Robilliard and Virginie Marion-Poty and
Cyril Fonlupt",
title = "Genetic programming on graphics processing units",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "4",
pages = "447--471",
month = dec,
note = "Special issue on parallel and distributed evolutionary
algorithms, part I",
keywords = "genetic algorithms, genetic programming, Graphics
processing units, GPU, Parallel processing",
ISSN = "1389-2576",
URL = "http://www-lil.univ-littoral.fr/~robillia/Publis/GPonGPU09.ps.gz",
doi = "doi:10.1007/s10710-009-9092-3",
size = "25 pages",
abstract = "The availability of low cost powerful parallel
graphics cards has stimulated the port of Genetic
Programming (GP) on Graphics Processing Units (GPUs).
Our work focuses on the possibilities offered by Nvidia
G80 GPUs when programmed in the CUDA language. In a
first work we have showed that this setup allows to
develop fine grain parallelization schemes to evaluate
several GP programs in parallel, while obtaining
speedups for usual training sets and program sizes.
Here we present another parallelization scheme and
optimizations about program representation and use of
GPU fast memory. This increases the computation speed
about three times faster, up to 4 billion GP operations
per second. The code has been developed within the well
known ECJ library and is open source.",
notes = "ECJ JNI Java Native Interface to CUDA. RPN. Thread
divergence. nVidia 8800 GTX. Sextic symbolic
regression, 6-mux and 11-multiplexor, intertwined
spirals, Mackey-Glass \cite{langdon:2008:eurogp}. Data
cache not faster???
Code via
http://www-lil.univ-littoral.fr/~robillia/GPUregression.html",
}