Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
VPR: A new packing, placement and routing tool for FPGA research
FPL '97 Proceedings of the 7th International Workshop on Field-Programmable Logic and Applications
AI Techniques for Game Programming
AI Techniques for Game Programming
FPGA-Based System Design
Mutation-crossover isomorphisms and the construction of discriminating functions
Evolutionary Computation
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
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When the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Although this might seem insignificant, those recombination (or crossover) operators that violate this principle can direct a search away from the region (in genotypic space) that contains the two parent genotypes. This is contrary to the task for which the recombination operator was originally developed and can be detrimental, so this paper introduces a visualization that can be used to detect violations of this principle. The methodology also inspired the development of a different approach to recombining permutations, and a brief case study shows that an alternative recombination operator that does not violate this principle can be used to achieve a performance improvement over previous attempts to optimize Field-Programmable Gate-Array placements using a genetic algorithm. We believe that this technique will be invaluable for developing additional recombination operators.