旅行商问题(TSP)的一种改进遗传算法

传统的序号编码遗传算法(GA)使用PMX、CX和OX等特殊的交叉算子，这些算子实施起来很麻烦。针对TSP问题的求解，提出了一种新的改进遗传算法：单亲进化遗传算法(PEGA)，PEGA是利用父体所提供的有效边的信息，使用保留最小边的方法进行个体的进化。与传统的遗传算法相比，PEGA算法弥补了它们的不足之处，简化了遗传算法。给出了PEGA算法的数值算例，仿真实验表明了该算法对于对称的TSP和非对称的TSP问题，都具有收敛速度快的特点，证明了该算法的有效性。     

基于GA的网络最短路径多目标优化算法研究

针对现有基于遗传算法(GA)优化的网络最短路径算法存在优化目标单一、遗传编码质量低、搜索策略间平衡性差、适应度分配效率与灵活性较低等问题,建立一种多目标优化最短路径自适应GA模型,提出了优先级编码和优先级索引交叉算子,引入了遗传算子参数的模糊控制机制和基于自适应加权的适应度分配方法.实验结果表明,该算法的准确性和稳定性高、复杂度合理,实现了对网络设计优化中多目标最短路径问题的高质量求解.     

基于GA的网络最短路径多目标优化算法研究

针对现有基于遗传算法(GA)优化的网络最短路径算法存在优化目标单一,遗传编码质量低,搜索策略间平衡性差$适应度分配效率与灵活性较低等问题,建立一种多目标优化最短路径自适应GA模型.提出了优先级编码和优先级索引交叉算子,引入了遗传算子参数的模糊控制机制和基于自适应加权的适应度分配方法.实验结果表明,该算法的准确性和稳定性高,复杂度合理,实现了对网络设计优化中多目标最短路径问题的高质量求解.

     

并行遗传算法研究及其应用

GA是一类基于自然选择和遗传学原理的有效搜索方法,它从一个种群开始,利用选择、交叉、变异等遗传算子对种群进行不断进化,最后得到全局最优解.但随着求解问题的复杂性及难度的增加,提高GA的运行速度便显得尤为突出,采用并行遗传算法(PGA)是提高搜索效率的方法之一.本文分析了并行遗传算法的四种模型,最后应用于0-1背包问题的求解.实验结果表明,该算法在具有较高搜索效率的同时,仍能维持很高的种群多样性.     

求解MSA问题的新型单亲遗传算法

多序列联配(MSA)在生物信息学研究中占有重要地位,MSA问题是一个典型的NP问题,遗传算法是求解NP完全问题的一种理想方法。文章针对MSA问题,提出了一种新型单亲遗传算法(PGA),不使用交叉算子,只使用变异和选择算子。并根据群体的多样性自适应调节变异概率,有效消除了算法中的欺骗性条件,使用灾变算子来确保算法的搜索能力。整个算法模拟了自然界进化的周期性,较好地解决了群体的多样性和收敛深度的矛盾。算法的分析和测试表明,该算法是有效的。     

基于遗传算法的电力系统VAR优化调度研究

电力系统无功功率(VAR)优化对电力系统的经济运行具有重要意义，文章在分析不同初始种群生成方法对GA性能的影响后，改进GA求解电力系统VAR优化调度问题。在VAR优化调度模型的基础上，构建了基于GA的VAR优化算法。利用GA对决策变量进行实数编码，使用二进制竞标赛策略选择子代个体，结合模拟二进制交叉(SBX)和多项式变异方案作为交叉算子和变异算子。评估了带借位减法指令(SWB)、Niderreiter准随机抽样(NQR)、非对齐系统(NAS)和高斯采样(GS)4种初始种群生成方法。通过对IEEE-30总线系统进行实验，结果表明：采用均匀抽样的SWB伪随机发生器生成初始种群的GA,在电力系统VAR优化调度计算得到的有功功率损耗最低，仅为4.00%。     

一种基于正态分布交叉的ε-MOEA

实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,简称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,简称NDX)算子.首先在一维搜索空间实例中对NDX与SBX算子进行比较和分析,然后将NDX算子应用于Deb等人提出的稳态多目标进化算法ε-MOEA(ε-dominance based multiobjective evolutionary algorithm)中.采用NDX算子的ε-MOEA(记为ε-MOEA/NDX)算法在多目标优化标准测试集ZDT和DTLZ的10个函数上进行了实验比较.实验结果和分析表明,采用NDX的?-MOEA所求得的Pareto最优解集质量明显优于经典算法ε-MOEA/SBX和NSGA-II.     

并行遗传算法在并行多机调度中的应用

GA是一类基于自然选择和遗传学原理的有效搜索方法,它从一个种群开始,利用选择、交叉、变异等遗传算子对种群进行不断进化,最后得到全局最优解。但随着求解问题的复杂性及难度的增加,提高GA的运行速度便显得尤为突出,采用并行遗传算法(PGA)是提高搜索效率的方法之一。本文分析了并行遗传算法的四种模型,最后将其应用于多机任务调度中。     

求解旅行商问题的一个改进的遗传算法

利用遗传算法求解TSP问题,通常需要使用PCX,CX和OX等特殊的交叉算子以提高算法的运行效率。针对自然数编码的方式,提出一种改进的遗传算法,即改进传统的顺序交叉算子,进行不相同子排列顺序交叉,使子代继承父代中优秀的子排列,加快算法的收敛速度。另外,采用没有重复的稳态繁殖避免早熟。实验结果表明,此改进算法对于TSP和DHC问题均具有较好的性能。     

基于遗传算法的移动机器人路径规划

本文提出的基于遗传算法的移动机器人路径规划,用栅格表示移动机器人的工作环境,采用序号编码和与此编码机制相适应的遗传操作算子,并增加了新的插入算子和删除算子,同时应用了最优保存策略,最后得到移动机器人在由栅格表示环境下的最短无碰路径.通过对算法进行仿真和实验,结果表明了所提算法的有效性和可行性.     

遗传算法在钟表机芯设计中的应用

在钟表机芯设计中，齿轮参数的优化设计是一个组合优化问题，很难用传统优化方法解决.遗传算法是一种基于生物进化原理的启发式搜索方法，近年来，它成功地解决了许多计算难题.使用该算法的难点是如何将具体问题映射成适于该算法的编码以及根据编码进行各种操作.该文对传动系统各齿轮参数序号进行编码，成功地解决了齿轮参数的优化设计问题，也为一般机械设计中传动系统参数的优化提供了经验.通过比较，利用遗传算法得出的参数比用专家系统得出的参数更优.     

Structural pattern recognition using genetic algorithms with specialized operators

This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices. The fitness of each solution string is computed by accumulating the similarity between the unary and binary attributes of the matched vertex pairs. We propose novel crossover and mutation operators, specifically for this problem. With these specialized genetic operators, the proposed algorithm converges to better quality solutions at a faster rate than the standard genetic algorithm (SGA). In addition, the proposed algorithm is also capable of recognizing multiple instances of any model object. An efficient pose-clustering algorithm is used to eliminate occasional wrong mappings and to determine the presence/pose of the model in the scene. We demonstrate the superior performance of our proposed algorithm using extensive experimental results.     

A fuzzy self-tuning parallel genetic algorithm for optimization

The genetic algorithm (GA) is now a very popular tool for solving optimization problems. Each operator has its special approach route to a solution. For example, a GA using crossover as its major operator arrives at solutions depending on its initial conditions. In other words, a GA with multiple operators should be more robust in global search. However, a multiple operator GA needs a large population size thus taking a huge time for evaluation. We therefore apply fuzzy reasoning to give effective operators more opportunity to search while keeping the overall population size constant. We propose a fuzzy self-tuning parallel genetic algorithm (FPGA) for optimization problems. In our test case FPGA there are four operators—crossover, mutation, sub-exchange, and sub-copy. These operators are modified using the eugenic concept under the assumption that the individuals with higher fitness values have a higher probability of breeding new better individuals. All operators are executed in each generation through parallel processing, but the populations of these operators are decided by fuzzy reasoning. The fuzzy reasoning senses the contributions of these operators, and then decides their population sizes. The contribution of each operator is defined as an accumulative increment of fitness value due to each operator's success in searching. We make the assumption that the operators that give higher contribution are more suitable for the typical optimization problem. The fuzzy reasoning is built under this concept and adjusts the population sizes in each generation. As a test case, a FPGA is applied to the optimization of the fuzzy rule set for a model reference adaptive control system. The simulation results show that the FPGA is better at finding optimal solutions than a traditional GA.     

Automated global structure extraction for effective local building block processing in XCS

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.     

求解TSP的交配算子设计策略

旅行商问题(TSP)是一类典型的NP完全问题,遗传算法(GA)是求解这类问题的常用方法之一.由于该问题的解是一种特殊的序列,一些典型的GA交配方法在求解该问题时的性能并不理想.通过多次对比两种常用的GA交配方法与3种专门为TSP作优化的交配方法,总结了一种对旅行商问题的交配算子的设计策略,即注重对双亲的边继承以及加入适当的贪心控制策略.通过对Gr17、Oliver30、Eil51、Eil76和Krob100等测试数据进行实验,证明了在该策略的指导下改进的两种交配算子具有更好的表现.     

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.     

An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes

In this study, a comprehensive empirical test is conducted to analyse the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover, Laplace crossover and simulated binary crossover operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos-enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations.     

Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks

In this paper, a Modified Topology Crossover (MTC), Energy-II and Energy-III mutations and Genetic Operator Combinations (GOCs) for integer coded Genetic Algorithm (GA) with sequence and topological representations are proposed to improve the efficiency of the GA for multicast routing in ad hoc networks. Combined lifetime improvement and time delay minimization are considered as objectives. To study the effect of genetic operators on the performance of multicast routing optimization problem, crossover methods such as sequence and topology crossover, topology crossover and mutation methods such as node mutation, energy mutation, inverse mutation and insert mutation are considered. Penalty parameter-less constraint handling scheme is used for handling the number of broken links which are identified during reproduction. The simulations are conducted on different size graphs generated using Waxman’s graph generator. Three case studies namely Case-1: Performance comparison of various crossover methods with node mutation, Case-2: Performance comparison of various mutation methods with the proposed MTC and Case-3: Performance comparisons of four GOCs are investigated. The above three cases are experimented with nonparametric statistical tests such as Friedman, Aligned Friedman and Quade. From these tests, it is proved that GOCs perform better for both large scale and small scale networks. These results also endorse that the proposed GOCs can be used to improve the GA for solving multicast routing problems more effectively.     

Virus coevolution partheno-genetic algorithms for optimal sensor placement

A virus coevolutionary partheno-genetic algorithm (VEPGA), which combined a partheno-genetic algorithm (PGA) with virus evolutionary theory, is proposed to place sensors optimally on a large space structure for the purpose of modal identification. The VEPGA is composed of a host population of candidate solutions and a virus population of substrings of host individuals. The traditional crossover and mutation operators in genetic algorithm are repealed and their functions are implemented by particular partheno-genetic operators which are suitable to combinatorial optimization problems. Three different optimal sensor placement performance index, one aim on the maximization of linear independence, one aim on the maximization of modal energy and the last is a combination of the front two indices, have been investigated. The algorithm is applied to two examples: sensor placement for a portal frame and a concrete arc dam. Results show that the proposed VEPGA outperforms the sequential reduction procedure (SRP) and PGA. The combined performance index makes an excellent compromise between the linear independence aimed index and the modal energy aimed index.