基于染色体易位的动态进化算法

针对采用二进制编码的进化算法在函数优化过程中会因为维度之间的相互干扰,导致部分低阶模式出现无法进行有效重组的现象,提出一种新的结合细胞学研究成果的进化算法——染色体易位的动态进化算法(CTDEA)。算法通过构建基因矩阵来模拟有机染色体在细胞内的结构化过程,并在基因矩阵的基础上对出现同质化的染色体短列实施模块化的易位操作,以此来维护种群的多样性;同时通过个体适应度划分种群的方式来维护精英个体,确保个体间的竞争压力,提升算法的寻优速度。实验结果表明,该进化算法与已有的遗传算法(GA)和分布估计算法相比较,在维护种群多样性方面有较大改进,能够将种群的多样性保持在0.25左右;且在寻优的精度、稳定性以及速度上也有明显的改进和提高。     

遗传交叉和变异对种群多样性的影响

首先,定义了群体的算术交叉扩展子空间、寻优空间和基因位直方图概念,并分析了交叉在解空间的扩展性．然后,证明了在二进制编码中,交叉不能改变基因层次上的多样性;而在实数编码中,在一定条件下,算术交叉可改变基因层次上的多样性,但以扩大寻优空间、产生无用解为代价．随后,证明了交叉可改变个体层次上的多样性,而变异可改变以上两个层次上的多样性．最后,分析了所得结论对遗传算法的改进和应用具有的指导意义,并通过仿真加以验证．     

一种并行变异自适应遗传算法及其性能分析

针对基于实数编码的遗传算法收敛速度慢与收敛精度不高等问题,通过定义种群活力,提出了一种改进的自适应遗传算法．该算法中,种群活力的定义综合考虑了种群多样性和相邻代种群间相似度,众数代替平均数作为新的种群适应度参考量,并依以上两点对交叉和变异概率进行自适应调节,同时引入并行机理对变异操作进行了改进．通过仿真实例,验证了该算法具有较高的收敛速度和求解精度．最后,该算法还被应用于解决汽油调和优化调度问题．     

基于交叉模型的改进遗传算法

提出一种解决早熟收敛问题的改进遗传算法. 通过最小生成树聚类将种群划分为若干个子种群, 子种群内的个体之间及不同子种群间的个体之间同时进行遗传操作. 同子种群间个体的遗传操作可以保证算法的进化方向和收敛速度, 不同子种群间个体的遗传操作可以避免近亲繁殖, 提供多样性. 分别采用二进制和实数编码, 在经典的 23 个基准函数上的对比测试结果表明, 所提出算法具有较好的收敛速度和寻优能力.

     

遗传算法的有效基因块保护策略*

遗传算法是一种结合全局搜索和局部搜索两种特性的自适应搜集随机算法,但存在早熟性收敛和收敛速度慢两方面问题。由于遗传算法运行过程中最小诱导模式普遍存在于个体中,同时在遗传算法运行后期,个体中存在很多属于收敛优化解或全局最优解的基因块。通过分析和论证,建立了保护属于最小诱导模式或优化解的有效基因块的控制策略。该策略可与其他杂交算子和变异算子结合,为遗传操作中父代个体包含的非有效基因块基因座上的基因提供更多进化机会,从而提高这些基因座上的有效基因数量,维持有效的种群多样性,较好地抑制了GA的早熟现象,提高了算法收敛速度和全局寻优能力。     

遗传交叉和变异对种群多样性的影响

首先,定义了群体的算术交叉扩展子空间、寻优空间和基因位直方图概念,并分析了交叉在解空间的扩展性．然后,证明了在二进制编码中,交叉不能改变基因层次上的多样性;而在实数编码中,在一定条件下,算术交叉可改变基因层次上的多样性,但以扩大寻优空间、产生无用解为代价．随后,证明了交叉可改变个体层次上的多样性,而变异可改变以上两个层次上的多样性．最后,分析了所得结论对遗传算法的改进和应用具有的指导意义,并通过仿
真加以验证．

     

基于随机基因交叉与多倍体策略的遗传算法

针对经典遗传算法的早熟及精度问题进行了研究,提出了一种基于随机基因实数交叉与多倍体策略的遗传算法。借鉴生物界中多倍体的概念,采用了实数编码并利用多倍体分别保存最优单体、保留单体及变异单体,从而组成多样性种群;选择操作采用了轮盘赌算法;交叉操作引入随机基因交叉概念。最后应用测试函数对算法进行测试,并与经典遗传算法进行了比较。仿真实验结果表明,该改进算法不仅保持了种群的多样性,有效抑制了早熟收敛,还降低了算法的复杂度,提高了搜索精度,使得算法能以较高的精度达到复杂高维度函数的全局最优。     

基于自适应交叉概率因子的差分进化算法及其应用

基本差分进化算法的控制参数在进化过程中是保持不变的,但是交叉概率因子的大小影响种群进化的 多样性以及种群的收敛速度．本文提出一种根据种群平均适应度方差非线性改变交叉概率因子的方法．在种群多样 性降低时增大该因子,使之接受更多变异个体的基因,有利于加强局部搜索和加速收敛速率;多样性增大时减小该 因子,避免该个体基因结构遭到过多的破坏,促使该个体的进化,有利于保持种群的多样性和完成全局搜索．并且 给出了一种新的变异方式,这种变异方式一方面能提高算法的收敛速度,另一方面能在一定程度上保持较高的种群 多样性．最后将其应用到热连轧精轧机组负荷分配优化中,改进后的优化方法在性能上要优于所对比算法．     

采用生物信息克隆的免疫算法

受克隆选择过程生物学原理的启发, 提出了一种采用生物信息克隆的免疫算法. 抗体克隆依赖于一个动态平衡的网络, 并与遗传因素相关. 为了解决传统克隆过程中信息不能充分利用的问题, 该进化算法将环境信息、抗体历史信息以及抗体遗传特征积累的影响引入人工免疫系统, 用这多种信息作为先验知识为克隆过程提供决策支持, 引导抗体系统的更新. 同时采用实数与二进制混合编码方式增加种群多样性, 提高收敛速度, 然后分析了该算法的收敛性. 仿真实验结果表明, 该克隆策略能较大的提高免疫克隆算法的优化能力; 与几种高级免疫克隆算法和进化算法相比, 该算法寻优精度高, 收敛速度快, 能有效的克服早熟现象, 并具有很好的高维优化能力.     

电力系统机组组合问题的动态双种群粒子群算法

针对标准粒子群优化算法易陷入局部最优点的缺点,提出了动态双种群粒子群优化算法（DDPSO）。该算法中两个子种群规模随进化过程不断变化,进化中分别采用不同的学习策略且相互交换信息。将该算法应用于机组组合问题中,采用实数矩阵编码方法对发电计划进行编码,将两层优化问题转化为单层优化问题,直接运用DDPSO算法求解。仿真结果表明,用该方法解决机组组合问题具有良好的精度和鲁棒性。     

A feature selection method based on modified binary coded ant colony optimization algorithm

Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.     

基于GA的飞行员模拟机排班问题求解

飞行员模拟机复训问题是一个多目标、多资源约束的排班问题,具有较高的复杂度,传统遗传算法无法有效求解该问题。为此,提出一种新的遗传算法,利用基因适应度对交叉、选择操作进行改进,以提高种群的多样性和进化性能。在仿真数据和真实数据上的实验结果表明,该算法有效提高了解的精度,加快了种群的收敛速度。     

Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes

Genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. Under their initial formulation, the search space solutions are coded using the binary alphabet, however other coding types have been taken into account for the representation issue, such as real coding. The real-coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains.A problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of population diversity. The mutation operator is the one responsible for the generation of diversity and therefore may be considered to be an important element in solving this problem. For the case of working under real coding, a solution involves the control, throughout the run, of the strength in which real genes are mutated, i.e., the step size.This paper presents TRAMSS, a Two-loop Real-coded genetic algorithm with Adaptive control of Mutation Step Sizes. It adjusts the step size of a mutation operator applied during the inner loop, for producing efficient local tuning. It also controls the step size of a mutation operator used by a restart operator performed in the outer loop, for reinitializing the population in order to ensure that different promising search zones are focused by the inner loop throughout the run. Experimental results show that the proposal consistently outperforms other mechanisms presented for controlling mutation step sizes, offering two main advantages simultaneously, better reliability and accuracy.     

面向算法的遗传算法类库设计

遗传算法个体数据结构复杂,随机性是其基本特性。建立一个标准的、开放的遗传算法类库有利于遗传算法的直接应用和在此基础上进一步扩充。本文提出用面向算法的模式将多种数据结构的遗传算法(二进制编码、实型编码、有序编码、变长串编码、遗传程序设计编码等五种算法)统一建模,实现了数据和算法分离,静态数据和动态数据分离,群体和个体分离。这样的类库设计结构更加清晰,实现了易用性、可扩充性与易调试性的统一,可用于对线性规划、旅行推销员问题、数据回归等问题的求解。     

组合优化问题中遗传算法的局限性及其改进模式

遗传算法在解决多峰函数求解，多目标规划和生产调度等问题时，相对其它优化算法具有一定的优势，但仍存在严重的局限性，尤其表现在组合优化的求解问题中，为此，提出一种“生物进化过程＝遗传操作＋免疫功能”的新模式，并通过生产调度的求解问题验证了该算法的有效性。     

Dynamic Parameter Encoding for Genetic Algorithms

The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models.     

A computerized causal forecasting system using genetic algorithms in supply chain management

Forecasting activities are widely performed in the various areas of supply chains for predicting important supply chain management (SCM) measurements such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, traffic costs in transportation management, and so on. This paper presents a computerized system for implementing the forecasting activities required in SCM. For building a generic forecasting model applicable to SCM, a linear causal forecasting model is proposed and its coefficients are efficiently determined using the proposed genetic algorithms (GA), canonical GA and guided GA (GGA). Compared to canonical GA, GGA adopts a fitness function with penalty operators and uses population diversity index (PDI) to overcome premature convergence of the algorithm. The results obtained from two case studies show that the proposed GGA provides the best forecasting accuracy and greatly outperforms the regression analysis and canonical GA methods. A computerized system was developed to implement the forecasting functions and is successfully running in real glass manufacturing lines.     

远亲杂交遗传算法及其在供应链优化中的应用

基于生物系统中普遍存在“远亲杂交优于近亲繁殖”的现象,提出了基于远亲杂交的遗传算法：远亲杂交保持演化群体良好的多样性,克服了遗传算法局部收敛的缺陷,提高了算法的全局搜索能力。针对供应链优化研究,本文提出了一个新型供应链优化模型,同时把新算法应用于求解该优化问题,结果表明,对供应链优化问题的求解,远亲杂交遗传算法优于基本遗传算法和分枝界定法。     

Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design

In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency.     

双链量子遗传算法的收敛性分析

基于实数编码和目标函数梯度信息的双链量子遗传算法可增加种群的多样性、扩大解空间的搜索域、加速算法的进化进程、避免早熟收敛现象,但没有从理论上证明该算法的收敛性。为此,给出相应的定理,利用定理从理论上证明该算法的收敛性,通过仿真实例,论述量子编码和量子旋转门对算法收敛性和优化效率的影响。结果表明,该研究丰富和完善了双链量子遗传理论。