基于遗传算法的全国科技资源配置效率分析
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摘要
采用指标体系法计算全国31个省(直辖市、自治区) 2008—2016年科技资源配置效率,结果显示全国科技资源配置效率总体呈逐年提高趋势,且呈现东部高而西部低的规律。应用遗传算法(genetic algorithm,GA)对全国科技资源配置进行优化,结果理论上科技资源配置效率可以达到4. 70。把最优的科技资源投入产出组合与京、沪、粤等城市进行比较分析,提示科技资源投入要精准化,并培育良好的市场经济环境,使科技产出最大化,提高科技资源配置效率。
The index system method is used to calculate the efficiency of scientific and technological resources allocation of the 31 provinces,province-level municipality and autonomous regions from 2008 to 2016. The results show that the efficiency of the allocation of science and technology resources in the whole country is increasing year by year,but the East is higher than the West. The genetic algorithm is used to optimize the allocation of national scientific and technological resources,and the allocation efficiency of science and technology resources could reach4. 70. The best technology resources allocation with Beijing,Shanghai and Guangdong are compared and analyzed. It is put forward that the input of science and technology should be precise. A good market economy environment should be cultivated,and maximized the output of science and technology. The efficiency of technology resources allocation could be improved.
The index system method is used to calculate the efficiency of scientific and technological resources allocation of the 31 provinces,province-level municipality and autonomous regions from 2008 to 2016. The results show that the efficiency of the allocation of science and technology resources in the whole country is increasing year by year,but the East is higher than the West. The genetic algorithm is used to optimize the allocation of national scientific and technological resources,and the allocation efficiency of science and technology resources could reach4. 70. The best technology resources allocation with Beijing,Shanghai and Guangdong are compared and analyzed. It is put forward that the input of science and technology should be precise. A good market economy environment should be cultivated,and maximized the output of science and technology. The efficiency of technology resources allocation could be improved.
引文
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[2]刘焕,胡春萍,张攀.省级政府实施创新驱动发展战略监测评估——一个分析框架[J].科技进步与对策,2015,32(8):128-132.
[3]黄海霞,张治河.基于DEA模型的我国战略性新兴产业科技资源配置效率研究[J].中国软科学,2015(1):150-159.
[4]范斐,杜德斌,李恒,等.中国地级以上城市科技资源配置效率的时空格局[J].地理学报,2013,68(10):1331-1343.
[5]徐林明.基于理想解法的区域自主创新能力评价与分析[J].莆田学院学报,2012,19(6):24-27.
[6]陈国生,杨凤鸣,陈晓亮,等.基于Bootstrap-DEA方法的中国科技资源配置效率空间差异研究[J].经济地理,2014,34(11):36-42.
[7]范斐,杜德斌,李恒.区域科技资源配置效率及比较优势分析[J].科学学研究,2012,30(8):1198-1205.
[8]彭建升.基于BPANN-IGA方法的中国区域科技资源配置效率优化分析[J].集宁师范学院学报,2018,40(3):16-19.
[9]范德成,杜明月.高端装备制造业技术创新资源配置效率及影响因素研究——基于两阶段Sto NED和Tobit模型的实证分析[J].中国管理科学,2018,26(1):13-24.
[10]王小鲁,樊纲,余静文.中国分省份市场化指数报告[M].北京:社会科学文献出版社,2017:256-257.
[11]王春杨,吴国誉.研发资源配置、溢出效应与中国省域创新空间格局[J].研究与发展管理,2018,30(1):106-114.
[12]高强,吕文芝,杜小山,等.遗传算法优化性能评价准则研究[J].西安交通大学学报,2006(7):803-806.