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.
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Yu, Q., Chen, C., Pan, Z. (2005). Parallel Genetic Algorithms on Programmable Graphics Hardware. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_134
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DOI: https://doi.org/10.1007/11539902_134
Publisher Name: Springer, Berlin, Heidelberg
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