Transition regions: Regional-national eco-innovation systems and strategies

The key phenomenon around which the paper is constructed, given its core interest in the transition from a fossil fuels energy regime to a zero emissions and/or renewable energy regime (sometimes also ‘post-hydrocarbons’ regime; Smith, 2008), is the notion and explanation of ‘transition regions’. These are sub-national territories, usually with some degree of devolved governance in the fields of innovation, economic development and energy that, for reasons to be demonstrated, act as regional ‘lighthouses’ for eco-innovation both to other regions and countries. These are the places that are subject to ‘learning visits’ by global policy-makers and other interested parties eager to learn how success was achieved. Because regions differ within state-systems, the case material is presented according to three kinds of political model. These are, first, the liberal market model, notably north America and the UK; second is the co-ordinated market model such as Germany and some Nordic countries; and third hybrids which have a reasonably entrepreneurial economic climate typical of the ‘liberal market’ model but strong welfare states, more typical of the ‘co-ordinated market’ model such as Denmark, a small state, and China, a large one. Finally, each of six cases will be presented in the sequence of the ‘system’ and regional governance of ‘transition regions’ first, with the nature and role of the national eco-innovation regime summarised afterwards where top-down governmental influence is clearly visible, even weakly. Also eco-innovations that are broadly in the same field, such as renewable energy production, or electric vehicle consumption are studied, giving maximum comparative value from their emergence in different countries and regions.     

自适应协同进化算法及其在创新设计中的应用研究*

提出一种自适应协同进化算法,对其进行了数学描述。设计了一个支持该算法的创新设计系统,为分布式环境下设计人员的协作和创新思路的开拓提供了支撑平台。算法中自适应学习的引入为在设计中自动而有效地使用先验智能提供了可行性。最后以一个建筑实例的设计为例对所述的方法和系统加以描述。     

基于双层演化的多人囚徒博弈研究

针对传统的多人重复囚徒博弈（NIPD）难以在大N值时涌现高合作率的问题进行研究,分析了NIPD模型在自由竞争模式和协议竞争模式下的博弈情况,类比2-IPD问题的“针锋相对”策略（TFT）,提出了“类TFT”的策略思想,并结合协同进化的理论,提出Agent及其聚集体Group分层演化的思想,建立了双层演化的仿真模型DL-NIPD。实验结果表明,自由竞争模式只适合小N值的合作,要从根本上保证任何N值下系统都能涌现很高的合作率,必须建立起双层的演化模式,通过显式的协议和团队的竞争,来促进微观主体的合作。     

A genetic algorithm for the simultaneous delivery and pickup problems with time window

This paper concerns a Simultaneous Delivery and Pickup Problem with Time Windows (SDPPTW). A mixed binary integer programming model was developed for the problem and was validated. Due to its NP nature, a co-evolution genetic algorithm with variants of the cheapest insertion method was proposed to speed up the solution procedure. Since there were no existing benchmarks, this study generated some test problems which revised from the well-known Solomon’s benchmark for Vehicle Routing Problem with Time Windows (VRPTW). From the comparison with the results of Cplex software and the basic genetic algorithm, the proposed algorithm showed that it can provide better solutions within a comparatively shorter period of time.     

控制参数协进化的差分进化算法及其应用

提出一种控制参数协进化的差分进化算法(DE-CPCE),实现算法控制参数随种群搜优进展,自适应动态调整。D E-CPCE算法将控制参数作为原始个体的共生个体,且每一个原始个体都有各自的共生个体;算法在对原优化问题进行差分进化搜优的同时,以原始个体进化效率作为共生个体(即控制参数)的评价,并通过共生个体的差分进化操作实现其协进化。D E-CPCE算法能随优化问题搜优进展,自适应动态调整算法控制参数,实时为算法搜优提供最优的控制参数。仿真研究表明,DE-CPCE算法的控制参数具有动态自适应性;并且在与文中所提及的算法(DE/rand/1,DE/best/1,DE/rand-to-best/1,DE/rand/2,DE/best/2,self-adaptive Pareto DE and self-adaptive DE)比较中,该算法能以较高概率求得全局最优值,且收敛速率快,求得最优解的精度高。同时,应用 DE-CPCE算法估计 SO2催化氧化反应动力学模型参数,结果优于文献报道。     

Modelling sustainability

The article presents a general classification of the models being developed in the area of sustainability arguing that the existing models represent the historical conceptualisation of sustainability starting from environmental constraints and moving towards economic valuation and social behaviour and policies. Coupled with computer power, sophisticated models with a varying levels of complexity have also been developed (static/dynamic; local/global; specific/general). However as any model is a simplification of the complex reality, the main purpose of any sustainability modelling (and the newly emerging area of sustainometrics) should be to allow dynamic representation, including the co-evolution of the sustainability systems and the role of humans as sustainability guardians.     

基于多目标协同进化的测试用例优先排序

测试用例优先排序是一种有效的降低回归测试开销的技术,通过对测试用例按照其重要程度排序后可获得更高的测试效率。针对传统多目标遗传算法在测试用例优化排序中存在的收敛较慢、易陷入局部最优、缺乏对不同测试准则的综合权衡等缺点,提出一种基于竞争模式的多目标协同进化算法。该方法采用平均代码覆盖率以及平均变异杀死率作为多个约束目标的测试准则来进行适应度度量,提高算法的错误检测率;使用个体绝对适应度与相对适应度对个体生存能力进行评价,衡量个体优秀程度,利用竞争性的协同进化思想加快算法收敛速度;通过剔除“老年”个体控制个体生存周期来避免陷入局部最优问题。同时,在影响算法执行效率的因素方面也进行了一系列的实验,结果表明该算法能够加快收敛速度,加强了局部搜索能力,相对于传统的优化算法来说具有更好的搜索效率和更高的错误检测率,从而验证了算法的有效性和可行性,证明了该算法具有一定的现实意义。     

基于多粒度共进化功能能推理的机械运动方案设计新方法

提出了基于多粒度共进化功能推理的机械运动方案设计方法.首先分析了共进化功能推理模型和多粒度设计模型的特点,并结合二者的优点构造了一种多粒度的共进化功能推理模型;然后将该推理模型应用于机械运动方案设计,提出了一种面向机械运动方案设计的共进化功能推理方法,该方法采用分类功能来描述机构单元及机械系统的运动特征信息,将机构单元和机械系统都采用运动功能变换函数进行抽象表达,通过功能推理来生成机械运动变换单元的串联组合方案;随即给出了相应的功能推理算法流程,通过与已有算法的比较详细分析了该算法的特性,并讨论说明了该算法所具有的效率高、可精确描述运动功能变换特性等优点;最后通过电线进给机构运动方案设计实例验证了该方法的有效性.     

变异协同进化的免疫克隆算法

利用免疫系统的克隆选择机制,提出一种用于函数优化的算法.算法的主要特点是:在迭代过程中,不仅抗体得到进化,同时建立变异向量集,令变异向量同步进化,协同工作,达到优化的目的.仿真实验表明,所提出的算法能以较快的速度完成给定范围的搜索和全局优化任务.     

Advances in evolutionary feature selection neural networks with co-evolution learning

Training neural networks is a complex task provided that many algorithms are combined to find best solutions to the classification problem. In this work, we point out the evolutionary computing to minimize a neural configuration. For this purpose, a distribution estimation framework is performed to select relevant features, which lead to classification accuracy with a lower complexity in computational time. Primarily, a pruning strategy-based score function is applied to decide the network relevance in the genetic population. Since the complexity of the network (connections, weights, and biases) is most important, the cooling state of the system will strongly relate to the entropy as a minimization function to reach the desired solution. Also, the framework proposes coevolution learning (with discrete and continuous representations) to improve the behavior of the evolutionary neural learning. The results obtained after simulations show that the proposed work is a promising way to extend its usability to other classes of neural networks.