GPGPU» Tag: Genetic Algorithms :: GPGPU.org

Competition: GPUs for Genetic and Evolutionary Computation

admin — Mon, 15 Jun 2009 22:27:47 +0000

This competition focuses on the applications of genetic and evolutionary computation that can maximally exploit the parallelism provided by low-cost consumer graphical cards. The competition will award the best applications both in terms of degree of parallelism obtained, in terms of overall speed-up, and in terms of programming style.

Submissions should be mailed to gecco2009@gpgpgpu.com no later than June 23, 2009. The final scores will be announced during GECCO. More information is available at the following sites.

http://www.gpgpgpu.com/gecco2009/
http://www.sigevo.org/gecco-2009/competitions.html

Entrants must submit (1) the application source code with instructions to compile it and (2) a two-page description of the application. Submissions will be reviewed by a committee of researchers from the evolutionary computation community and from industry. Each reviewer will score the submission according to 12 criteria concerning the submitted algorithm, the speed-up it achieves, and its impact on the evolutionary computation community. The total score will be obtained as the weighted sum of the 12 separate scores.

CIGPU 2009 Montreal 8-18 July 2009

admin — Tue, 18 Nov 2008 09:13:39 +0000

Following the success of the first Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU 2008, held in Hong Kong last June), next summer there will be another CIGPU event in Montreal. The workshop will contentrate on genetic and evolutionary computation running on GPUs, games’ consoles, mobile phones and other consumer electronics platforms. In addition to CIGPU 2009 there will be a tutorial on GPGPU given by Prof. Wolfgang Banzhaf and a GPGPU contest sponsored by nVidia. (CIGPU 2009 Website)

CIGPU 5 June 2008 Hong Kong additional technical discussion

Mark Harris — Sun, 25 May 2008 09:04:32 +0000

In addition to the papers already announced, Dr. Simon Harding (Memorial University, Newfoundland) and Dr. Tien-Tsin Wong (The Chinese University of Hong Kong) will lead a discussion on the practicalities of running evolution on modern graphics cards. They will contrast the current leading GPGPU tools considering ease of use, and support for debugging and performance monitoring. CIGPU will close with a short session considering the future of computational intelligence on GPUs.

A SIMD interpreter for Genetic Programming on GPU Graphics Cards

Mark Harris — Wed, 02 Apr 2008 08:34:41 +0000

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. (A SIMD interpreter for Genetic Programming on GPU Graphics Cards. W.B. Langdon and W. Banzhaf. In M. Neill, L. Vanneschi, A.I. Esparcia Alcazar, S. Gustafson eds., EuroGP 2008, pp73-85. Springer, LNCS 4971, 26-28 March, Naples.)

Evolutionary Computation on GPUs

Mark Harris — Fri, 29 Jul 2005 04:54:00 +0000

Genetic Algorithms (GA) comprise a class of evolutionary computation (EC). A difficulty with GA is that the traditional crossover operation introduces order-dependency and hence an increase in rendering passes on SIMD GPUs. To parallelize EC on GPUs, this project proposes to use another class of EC called Evolutionary Programming (EP), which applies mutations locally. The project studies in-depth how to efficiently map an EP algorithm to SIMD GPUs, including a scalable and visualizable genome map, mutation, tournament and selection, and finally convergence visualization. Intensive experiments and careful comparisons are conducted to demonstrate its performance speedup and accuracy. The project also shows that it is conceptually wrong and infeasible to generate high-quality random numbers on the current generation of GPUs and that the low-quality random numbers will lead to poor performance of EC. (K. L. Fok, T. T. Wong, and M. L. Wong, “Evolutionary Computing on Consumer-Level Graphics Hardware”, IEEE Intelligent Systems, and “Parallel Evolutionary Algorithms on Graphics Processing Unit” in Proc. of IEEE Congress on Evolutionary Computation 2005.)

Parallel Genetic Algorithms on Programmable Graphics Hardware

Mark Harris — Thu, 26 May 2005 14:59:00 +0000

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 PCs. The approach stores chromosomes and their fitness values in texture memory on the graphics card. Both fitness evaluation and genetic operations are implemented entirely with fragment programs executed on the GPU in parallel. The paper demonstrate the effectiveness of this approach by comparing it with a compatible software implementation. The presented approach benefits from the advantages of parallel genetic algorithms on a low-cost platform. (http://www.cad.zju.edu.cn/home/yqz/)