Genetic_Algorithm_for_Mapping

代码说明：

  采用遗传算法来实现任务图到多核处理器的映射功能。遗传算法是计算机科学人工智能领域中用于解决最优化的一种搜索启发式算法，是进化算法的一种。这种启发式通常用来生成有用的解决方案来优化和搜索问题。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。这个程序通过遗传算法那实现任务图到多核的映射的路径最优。(Using genetic algorithms to achieve the task graph mapping to multicore processors . Genetic algorithms are used in the field of artificial intelligence in computer science to solve an optimization heuristic search algorithm , an evolutionary algorithm . This heuristic is typically used to generate a useful solution to optimization and search problems. Evolutionary algorithm was originally borrowed from evolutionary biology and developed a number of phenomena , these phenomena include genetic mutation , natural selection and hybridization . Genetic algorithms are typically implemented as a computer simulation methods . For an optimization problem , a number of candidate solutions ( called individuals ) an abstract representation of the population ( called chromosomes ) to evolve a better solution . Traditionally, the solution of the binary representation ( i.e., a string of 0 and 1 ) , but can also be expressed by other methods . Evolved from a completely random population of individua)

文件列表：

inpara.txt,548,2014-01-06
mapping2.cpp,103923,2014-01-06
noctask_9.eps,2823,2014-01-06
noctask_9.tgff,3862,2014-01-06
noctask_9.tgffopt,801,2014-01-06
noctask_9.vcg,945,2014-01-06
readctg.cpp,12736,2014-01-06
readctg.h,982,2014-01-06