现有的D em pster组合规则及大多数改进规则对两个证据之间存在较大冲突时能有效处理,但是对只存在较小冲突的大量信息源的研究较少,而这类较小的冲突在大量信息源的条件下可能变得很大。本文对信源数目较大时各种组合规则的极限性能进行了分析和研究,给出了组合规则极限性能的评价标准,指出Sm ets,Y ager和D.P.等规则不适合处理大量信息源的情形,并且提出在大量信息源情况下,可依据不同的结果要求,选择组合规则的策略。
Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc, in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily ＂evolutionary＂. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.
