传统可拓策略生成方法由于缺乏有效的可拓变换运算式优化机制，在处理复杂不相容问题时的效率无法保证。为此，本课题拟基于可拓学与基因表达式编程(GEP)的理论与方法，研究获取能够化解问题的最优可拓变换运算式的机制与方法，以提高处理复杂不相容问题的效率。利用“可拓信息-知识-策略形式化体系”，研究多条件不相容问题核问题的可拓模型、条件相关网的形式化模型以及相应的传导推理规则，形成复杂不相容问题的求解模型；通过研究GEP个体的结构、多生成对象可拓变换的GEP基因解码模式以及GEP各遗传算子在可拓变换运算式重构中的作用机理，建立可拓变换运算式的自组织优化机制，最终形成一套基于GEP的可拓策略自组织生成理论与方法。本课题的研究将完善可拓策略生成的理论体系，预期研究成果可显著提升可拓策略生成系统的运行效率和智能化水平，推动人工智能领域中问题求解技术的进一步发展，将有广阔的应用前景。

Traditional extension strategy generating method cannot process complicated incompatible problems efficiently, due to the lack of effective extension transformation operation optimization mechanism. Thus, this project aims to study mechanisms and methods for obtaining optimal extension transformation operations that can solve the problem, using the theories and methods of Extenics and GEP, so that the efficiency of processing complicated incompatible problems can be enhanced. Use the “Extension Information-Knowledge-Strategy Formalized System” to study the extension model of core problem construction for multiple condition incompatible problem, formalized model of the condition correlative network and conductive reasoning rule under the condition correlative network, forming the solving model for complicated incompatible problems; by studying structure of GEP individual, GEP gene decoding mode for multiple generating object extension transformation, mechanism of GEP genetic operators in the reconstruction of extension transformation operations, establish extension transformation operation self-organized optimization mechanism and finally form a set of GEP based extension strategy self-organized generating theories and methods. This research will complete the theoretical system of extension strategy generating. Expectant research achievements will remarkably improve the running efficiency and intelligence level of extension strategy generating systems, boosting further development of problem solving technology in the artificial intelligence field, and can be applied widely.