Lake Eutrophic Evaluation Based on Bee Immune Evolutionary Algorithm

In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.

Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism

Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIEA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way.

An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation

A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.

Immune Genetic Algorithm for Optimal Design

A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.

Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

Self-adaptive learning based immune algorithm

A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.

ERBF network with immune clustering

Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.

基于免疫进化算法的计算机课程线上教学资源推荐方法

传统计算机课程线上教学资源推荐方法的平均绝对误差较高,为此设计基于免疫进化算法的线上教学资源推荐方法。利用免疫进化算法构建线上教学资源推荐模型,设计计算机课程线上教学资源推荐引擎,完成线上教学资源推荐。实验结果表明,该方法的平均绝对误差较低,优于对照组,具有可靠性。

Evolutionary Origin of Religions and Religious Evolution: Religious Neurosociology

The paper proposes that the evolutionary origin of religions is based on theory of mind as the product of interdependent division of labor between the forest specialist group (women and small children) and the woodland specialist group (men) in early hominins who lived in the mixed forest-woodland habitat. To complement each other’s work without interfering each other’s work, one specialist group had to recognize (imagine) that the other specialist group existed to think for themselves and to do different works. The result was theory of mind which is to recognize (imagine) that the others exist to think for themselves. (The forest-woodland groups became the hunter-gatherer groups for the Homo species in the savanna habitat.) Under existential pressure, hominins invented imaginary specialists as imaginary agents who existed to think for themselves and to do different works in imaginary division of labor to enhance survival chance. The result was religion with imaginary behaviors. Therefore, religion is defined as a set of beliefs and behaviors based on theory of mind that produces a shared imagination to enhance survival chance under existential pressure. This paper proposes that the religious evolution consists of the premodern imaginative religion for local society habitat starting from bipedalism, the modern rational imaginative religion for regional society habitat starting from the Axial Age, and the postmodern diverse rational imaginative religion for global society habitat starting from the Information Revolution. In conclusion, the religious brain is the imaginative brain, and the religious social behaviors are imaginary social behaviors. The religious evolution is the evolution of human imagination to enhance survival chance under existential pressure, such as the religious reinforcement of social bonds to enhance the survival chance of social group and the religious relief of stress and anxiety to enhance the survival chance of individuals.

Application of Particle Swarm Algorithm in the Optimal Allocation of Regional Water Resources Based on Immune Evolutionary Algorithm

The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources.

The sea bass Dicentrarchus labrax as a marine model species in immunology:Insights from basic and applied research

This review summarizes the current knowledge on immune defence activities of the European sea bass Dicentrarchus labrax by reporting the consistent amount of work done on this economically-important species.A draft genome sequence is available for this species,together with whole transcriptomes from lymphoid and non-lymphoid tissues.Available full-length coding sequences of many immunoregulatory and immune-related genes allow for targeted quantitative PCR analysis,nowadays needed for-omics data verification,ex vivo and in vitro.The first anti-T cells monoclonal antibody teleost-wise was obtained in sea bass,followed by several monoclonal and polyclonal markers of lymphocyte populations,namely T cells(pan-T,CD3ε,TcRγ,CD45),and B cells(IgM,IgT,IgD).The combined use of molecular and biochemical tools enabled investigations on innate and acquired immune responses of sea bass in unstimulated/stimulated fish,along the development and under variable environmental conditions and food regimes.An overview of sea bass viral and bacterial pathogens and available vaccines against these microorganisms is also provided.The knowledge accumulated in the past 25 years validates the European sea bass as a reference marine model in the field of fish immunology.

人工免疫算法及其应用

阐述了人工免疫系统的基本概念,讨论了几种典型的算法,包括基于免疫系统基本机制的免疫算法、基于免疫特异性的否定选择算法、基于免疫系统克隆选择理论的克隆选择算法、基于接种疫苗及免疫多样性的免疫进化算法、AIS与神经网络混合智能算法和模糊免疫系统等;以年代为序简述了AIS发展历史,介绍了AIS在若干具有代表性的领域中的应用情况。最后通过对AIS的特性和存在问题的分析,展望了今后的研究重点和发展趋势。

人工免疫系统及其算法

该文阐述了人工免疫系统(AIS)的基本概念,讨论了几种典型的算法,包括基于免疫系统基本机制的免疫算法,基于免疫特异性的否定选择算法,基于免疫系统克隆选择理论的克隆选择算法,基于接种疫苗及免疫多样性的免疫进化算法,AIS 与神经网络混合智能系统和模糊免疫系统以及威胁模型等。简述了AIS 发展历史,按年代顺序介绍了AIS 在若干具有代表性的领域中的应用情况。最后通过对AIS 的特性和存在问题的分析,展望了今后的研究重点和发展趋势。

免疫进化聚类算法

本文在分析经典聚类算法和基于遗传的聚类算法的优越性与存在不足的基础上 ,提出了一种新的聚类算法———免疫进化聚类算法 .该算法不仅有效地克服了经典聚类算法易陷入局部极小值和对初始化敏感的缺点 ,并且减轻了基于遗传的聚类算法在遗传后期的波动现象 .仿真实验表明 ,该算法的聚类正确率比基于遗传的聚类算法平均高 8～

用于约束优化的人工免疫响应进化策略

基于克隆选择学说及生物免疫响应过程的相关机理,探讨一种新的人工免疫系统模型———人工免疫响应,提出用于解决约束优化问题的人工免疫响应进化策略;基于算法网络拓扑结构的分析表明,新算法比传统的进化策略(μ,λ)-ES具有更大的收敛概率.对10个标准测试问题的测试结果表明,与采用随机排序的进化策略和采用动态惩罚函数的进化策略相比,新算法在收敛速度和求解精度上均具有一定的优势.

免疫进化算法及其在暴雨强度公式参数优化中的应用

在研究现有进化算法的优越性与存在不足的基础上,受生物免疫原理的启发,提出了一种新的算法———免疫进化算法。免疫进化算法的核心在于充分利用最优个体的信息,在整个进化过程中,以父代最优个体为基础来产生子代群体,并以最优个体的收敛来代替群体的收敛;此外,算法的随机搜索是在确定方式的指导下完成的。相比于现有的进化算法,免疫进化算法提高了收敛速度,有效地克服了不成熟收敛,理论证明该算法是全局收敛的。最后,用免疫进化算法对暴雨强度公式参数进行了优化,并将其计算结果与传统方法和加速遗传算法的计算结果作了比较,结果表明:免疫进化算法的拟合效果最好。

工业机器人的最优时间与最优能量轨迹规划

提出一种工业机器人的最优轨迹规划方法。将机器人的轨迹视为由机器人关节空间中一系列的关键点构成,关键点两两之间通过三次多项式曲线进行连接。通过使用加权系数法定义代价函数,从而使机器人运动过程中的总动作时间和消耗能量在某种程度上达到综合最优,同时考虑关节速度、加速度、二次加速度以及力或力矩等约束条件。在代价函数的设计中,采用一种新颖的罚函数排序形式来处理约束问题。提出基因环境双演化免疫克隆算法对所定义的代价函数进行优化。以上策略的采用,使算法具备一定的学习能力,增强算法的全局搜索能力,从而提高解的质量和算法效率。对斯坦福机器人的仿真结果表明了本文方法与现有方法相比,具有更高的搜索效率,能得到性能更良好的解。

免疫量子进化算法

将免疫的概念和方法引入到量子进化算法中,提出一种新型的进化算法——免疫量子进化算法。该算法在保留原算法优良特性的前提下,力图有选择、有目的地利用待求问题中的一些特征信息或先验知识,抑制或避免求解过程中的一些重复或无效的工作,以提高算法的整体性能。对背包问题的仿真实验表明,免疫量子进化算法的性能优于经典的进化算法、免疫进化算法、量子进化算法等3种算法。

一种新的免疫进化算法及其性能分析

基于免疫系统中的进化机理,提出了一种免疫进化算法。首先引入了邻域概念,并通过定义扩展半径和突变半径两个新算法参数而构造了较小和较大两个邻域。进而给出了扩展和突变操作分别利用这两个邻域进行局部和全局搜索,实现了从全局到局部的两层邻域搜索机制。分析了算法的优化机理和收敛性。仿真结果表明该算法具有不易陷入局部最优、解的精度高、收敛速度快等优点。