Co-evolution as a computational and cognitive model of design

Co-evolutionary design has been developed as a computational model that assumes two parallel search spaces: the problem space and the solution space. The design process iteratively searches each space using the other space as the basis for a fitness function when evaluating the alternatives. Co-evolutionary design can also be developed as a cognitive model of design by characterizing the way in which designers iteratively search for a design solution, making revisions to the problem specification. This paper presents the computational model of co-evolutionary design and then describes a protocol study of human designers looking for evidence of co-evolution of problem specifications and design solutions. The study shows that co-evolutionary design is a good cognitive model of design and highlights the similarities and differences between the computational model and the cognitive model. The results show that the two kinds of co-evolutionary design complement each other, having strengths in different aspects of the design process. Electronic Publication     

An immune inspired co-evolutionary affinity network for prefetching of distributed object

In distributed object computing (DOC) containers, cache strategy as a passive approach improves the performance by caching the recently accessed objects. By the advent of large-scale enterprise applications of DOC, caching methods fail to keep pace with the increasing importance of performance and the increasing scale of DOC system. Prefetching is an effective approach to improve the performance. It generates and stores pages or objects in caches in advance, by predicting the requests. In the current DOC container, prefetching strategy is not supported and seldom studied in the literature. The immune system with faster secondary response and its affinity network inspire applying the immune mechanisms to build holistic model of DOC for performance improvement. In this study, a co-evolutionary affinity network (CEA-Net) is proposed for prefetching distributed objects. In CEA-Net, objects are antibodies and computation tasks are antigens. Invocation relations among object classes are modeled by the immune network of antibody genotypes. Multiple affinity measures among antibody, antigen, genotypes of antibody and antigen, genotype set and antibody population are defined to model the complex relations among distributed objects. Especially in the antibody population, immune principles including clonal proliferation, immune memory, immune toleration and elimination are designed to add evolutionary features to the antibody population. Based on CEA-Net, the prefetching architecture for DOC is built including 5 main procedures, Network Abstractor, Access Recorder, Object Factory, Cache Engine and Prefetch Engine. Finally, the experimental study shows the promising access performance and the evolutionary features of CEA-Net. CEA-Net is instructive for the future design of high performance DOC containers, such as WebSphere Application Server and BEA WebLogic Application Server.     

Disc cutters plane layout design of the full-face rock tunnel boring machine (TBM) based on different layout patterns

The quality of plane layout design of the disc cutters for the full-face rock tunnel boring machine (TBM) directly affects the balance of force distribution on the cutter head during the excavating. Various layout patterns have been adopted in practice during the layout design of the disc cutters. Considering the engineering technical requirements and the corresponding structure design requirements of the cutter head, this study formulates a nonlinear multi-objective mathematical model with multiple constraints for the disc cutters plane layout design, and analyses the characteristics of a multi-spiral layout pattern, a dynamic star layout pattern and a stochastic layout pattern. And then a genetic algorithm is employed to solve a disc cutters’ multi-spiral layout problem, and a cooperative co-evolutionary genetic algorithm (CCGA) is utilized to solve a disc cutters’ star or stochastic layout problems. The emphasis was put on the study of superiority of three different layout patterns. Finally, an instance of the disc cutters’ plane layout design was solved by the proposed methods using three different kinds of layout patterns. Experimental results showed the effectiveness of the method of combining the mathematical model with the algorithms, and the pros and cons of the three layout patterns.     

一种基于双精英种群的协同进化算法研究

针对传统带精英策略的多目标进化算法种群收敛分布不够均匀,全局搜索能力不足的缺点,提出一种基于双精英种群的协同进化算法DEPEA(Double Elite Populations Co-evolutionary Algorithm)。该算法借鉴了子区间划分和非支配排序思想,将整个种群划分成两个不同级别的精英种群和一个普通种群;两个精英种群结合协同进化思想分别采用不同的进化策略实现对算法的探究和探查能力的平衡,高级别的精英种群与低级别的精英种群采用协作操作,促进更优秀的个体产生;高级别的精英种群与普通种群采用引导操作,加快普通个体向精英个体逼近。通过对五个标准的测试函数进行实验,并与传统的NSGA-II算法和最新的hybird_MOEA算法结果进行比较与分析,表明该算法不仅具有更好的全局收敛性,且能够更好地保证种群的多样性。     

基于混合协同进化算法的Web服务组合演化策略

为了在服务组合演化过程中高效地选择满足服务请求的Web服务,提出了一种基于混合协同进化算法的Web服务组合演化策略。首先,利用改进模糊C均值聚类算法将Web服务演化单元按应用分类;然后,利用带权值的粒子群算法对划分好的子群进行内部择优;最后,对各个子群进行协同进化,使得针对用户服务请求,能够选出最优Web服务演化组合。实验结果表明,混合协同进化算法无论在算法稳定性或是算法运算时间上都优于传统协同进化算法,且对于Web环境下大量的服务请求能够提供优质、高效的服务。     

Co-evolutionary particle swarm optimization to solve constrained optimization problems

This paper presents a co-evolutionary particle swarm optimization (CPSO) algorithm to solve global nonlinear optimization problems. A new co-evolutionary PSO (CPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, based on the theory of extrapolation, the induction of evolving direction is enhanced by adding a co-evolutionary strategy, in which the particles make full use of the information each other by using gene-adjusting and adaptive focus-varied tuning operator. Infeasible degree selection mechanism is used to handle the constraints. A new selection criterion is adopted as tournament rules to select individuals. Also, the infeasible solution is properly accepted as the feasible solution based on a defined threshold of the infeasible degree. This diversity mechanism is helpful to guide the search direction towards the feasible region. Our approach was tested on six problems commonly used in the literature. The results obtained are repeatedly closer to the true optimum solution than the other techniques.     

仿生蜥蜴行为的三目标协同进化算法及机构设计应用

对3种雄性侧边斑点蜥蜴的行为特点和繁衍生存机理进行仿生,提出一种求解三目标优化问题的协同进化算法.将三个设计目标视为3种蜥蜴,将设计变量映射为蜥蜴种群的染色体,采用计算影响因子和模糊聚类方法,将染色体分割为反映3种蜥蜴各自遗传因素的基因段,根据3种蜥蜴的行为特点,建立各自适应函数与三个目标函数的映射关系,用于评价3种蜥蜴个体适应自然的能力.3种蜥蜴分别以自身适应函数为目标进行协同进化,获得各自的最佳基因.3种蜥蜴的最佳基因组成一个新染色体,并根据收敛判别,通过多代进化,获得最好的染色体(解).以补偿滑轮组变幅机构的三目标优化设计为例,仿真计算结果表明了本算法的有效性.     

多目标问题的游戏模型并行共同进化算法

游戏理论的主要成就是进化稳定战略,由MaynardSmith在1982年提出。使用基于游戏模型的共同进化算法寻找ESS作为多目标问题(MOPs)的解,该算法是一种基于粗粒度并行模型的进化算法。首先,研究游戏模型的共同进化方法解决MOPs的有效性。且说明进化游戏如何由共同进化算法来具体实现,证实它是否能达到MOP的最佳均衡点。其次,通过在几个多目标问题上的严格的实验,与其它一些方法比较,评估该方法的性能。     

The Impact of Payoff Function and Local Interaction on the N-Player Iterated Prisoner's Dilemma

The N-player iterated prisoner's dilemma (NIPD) game has been widely used to study the evolution of cooperation in social, economic and biological systems. This paper studies the impact of different payoff functions and local interactions on the NIPD game. The evolutionary approach is used to evolve game-playing strategies starting from a population of random strategies. The different payoff functions used in our study describe different behaviors of cooperation and defection among a group of players. Local interaction introduces neighborhoods into the NIPD game. A player does not play against every other player in a group any more. He only interacts with his neighbors. We investigate the impact of neighborhood size on the evolution of cooperation in the NIPD game and the generalization ability of evolved strategies. Received 18 August 1999 / Revised 27 February 2000 / Accepted 15 May 2000