An adaptive real-coded genetic algorithm

Selecting operators, selection strategy, and tuning parameters for genetic algorithms (GAs)is usually a very time-consuming job. In this article we introduce a method for developing an adaptive real-coded genetic algorithm (ARGA) which aims at reducing this computation time. In developing the algorithm, we first use factorial design experiments to identify ''important'' and ''sensitive'' parameters. Then these parameters will be dynamically changed during the evolutionary process by efficient computing budget allocation. At the end of the search process, not only has the optimum of the original problem been found, but also the adaptive changing pattern of the GA parameters has been captured. This algorithm is successfully used to solve some benchmark problems, a Linear-Quadratic-Gaussian (LQG) problem and a drug scheduling problem. The results show that ARGA outperforms simple GAs and other adaptive GAs which use the same type of operators. Moreover, ARGA is able to find the optimum for some difficult problems while the simple GAs with the best parameter combination can only reach the local optimum.     

Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology

The goal of this work is to propose a novel approach to function optimisation by evolutionary techniques, in particular, real-coded genetic algorithms. A new genetic crossover operator, suitable for real codification, has been designed. This operator is called morphological crossover as it is based on mathematical morphology theory. The morphological crossover includes a new genetic diversity measure that has low computational cost. This operator is presented along with the resolution of a set of optimisation problems, including neural network training. The results are compared to other optimisation approaches as gradient descent methods or binary and real-coded genetic algorithms using different crossover operators. These tests show that the properties exhibited by the proposed operator when using real-coded genetic algorithms give higher convergence speed and less probability of being trapped in a local optimum.     

Gradual distributed real-coded genetic algorithms

A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the so-railed heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed real-coded genetic algorithms, a type of heterogeneous distributed real-coded genetic algorithms that apply a different crossover operator to each sub-population. Experimental results show that the proposals consistently outperform sequential real-coded genetic algorithms     

一种区域转移实数遗传算法

在讨论实数遗传算法基本遗传算子及参数选择的基础上，针对算法能快速找到较好解的特点及恰当的搜索范围能保证算法找到满意解的现象，将区域划分与转移思想应用到算法结构改进中，设计了一种改进的实数遗传算法。文中对新算法进行了细致描述，并与类似方法进行了比较。最后对多个实验函数进行寻优，优化实验结果证明，新方法在寻找复杂问题的全局解、提高搜索精度方面比基本实数遗传算法有较大的改进。文章最后对新算法的优缺点进行了总结。     

求解全局优化问题的混合智能算法

把序列二次规划作为遗传算法的一个局部搜索算子,嵌入到实数编码遗传算法中,构成一种基于序列二次规划和实数编码遗传算法的高效的混合智能算法。该方法充分利用序列二次规划法的强局部搜索能力和遗传算法的全局收敛性,使得混合算法的全局收敛性得到改善并且减少了计算量。数值实验结果表明,混合算法是高效可靠的。     

Comparing performances of backpropagation and genetic algorithms in the data classification

Artificial neural networks (ANN) have a wide ranging usage area in the data classification problems. Backpropagation algorithm is classical technique used in the training of the artificial neural networks. Since this algorithm has many disadvantages, the training of the neural networks has been implemented with the binary and real-coded genetic algorithms. These algorithms can be used for the solutions of the classification problems. The real-coded genetic algorithm has been compared with other training methods in the few works. It is known that the comparison of the approaches is as important as proposing a new classification approach. For this reason, in this study, a large-scale comparison of performances of the neural network training methods is examined on the data classification datasets. The experimental comparison contains different real classification data taken from the literature and a simulation study. A comparative analysis on the real data sets and simulation data shows that the real-coded genetic algorithm may offer efficient alternative to traditional training methods for the classification problem.     

Investigation of self-organizing map for genetic algorithm

This paper describes self-organizing maps for genetic algorithm (SOM-GA) which is the combinational algorithm of a real-coded genetic algorithm (RCGA) and self-organizing map (SOM). The self-organizing maps are trained with the information of the individuals in the population. Sub-populations are defined by the help of the trained map. The RCGA is performed in the sub-populations. The use of the sub-population search algorithm improves the local search performance of the RCGA. The search performance is compared with the real-coded genetic algorithm (RCGA) in three test functions. The results show that SOM-GA can find better solutions in shorter CPU time than RCGA. Although the computational cost for training SOM is expensive, the results show that the convergence speed of SOM-GA is accelerated according to the development of SOM training.     

面向深空探测任务的实数遗传编码多星任务规划算法

针对面向深空探测任务的多星任务规划问题,综合考虑卫星对目标时间窗口、卫星姿态机动以及工作能耗等约束条件,建立了面向深空探测任务的多星任务规划问题模型,针对常规01编码在进行大规模卫星任务规划时,存在的编码长度过长等问题,提出了一种基于实数编码方式的遗传算法,以求解面向深空探测的多星任务规划问题.该算法采用了一种以目标为染色体的实数编码方式,相比传统的以时间窗口为染色体的01编码方式,缩短了染色体长度,可有效提高算法的求解效率.通过仿真算例分析,验证了基于实数编码的遗传算法对求解多星任务规划问题的正确性、合理性和有效性,并将其与基于传统01编码方式的遗传算法进行对比分析,其结果表明基于实数编码方式的遗传算法在寻优能力和计算速度上具有明显优势,这为求解面向深空探测任务的多星任务规划问题提供了一种新的思路和方法.     

基于实数编码遗传算法的神经网络优化设计

提出一种基于综合控制策略的改进的实数编码遗传算法,用该算法对前向神经网络的结构及权值进行优化。通过实验结果表明,该算法能快速有效的确定网络的结构及权值。     

多维实数编码遗传算法

系统地分析了几种常见编码策略,在此基础上提出 一种新的编码策略--多维实数编码。仿真结果验证了这种新编码方式的有效性与合理性。     

用基于实数编码的自适应遗传算法进化神经网络

为了提高进化神经网络的速度,文章提出了一种基于实数编码方案的的遗传算法,该算法中采用了自适应变 异算子。用于求解ＸＯＲ问题,结果表明该算法具有很好的收敛性能。     

Real-coded memetic algorithms with crossover hill-climbing

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.     

An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals.     

Optimising a complex discrete event simulation model using a genetic algorithm

A steelworks model is selected as representative of the stochastic and unpredictable behaviour of a complex discrete event simulation model. The steel-works has a number of different entity or object types. Using the number of each entity type as parameters, it is possible to find better and worse combinations of parameters for various management objectives. A simple real-coded genetic algorithm is presented that optimises the parameters, demonstrating the versatility that genetic algorithms offer in solving hard inverse problems.     

实数编码遗传算法的前向神经网络优化设计

提出一种综合控制策略的实数编码遗传算法,用该算法实现对前向网络结构及权值的同时优化设计。用非线性函数的逼近问题作仿真实验。结果表明,该算法能快速有效地确定网络结构及权值。     

一种求解全局优化问题的新混合遗传算法

把简化的二次插值法融入实数编码遗传算法,构成适于求解全局优化问题的混合遗传算法,该混合算法可以较好解决遗传算法的早熟收敛问题,提高了收敛速度,改善了解的质量,并减少了计算量.由于该混合遗传算法对目标函数的性质没有要求,适合求解大规模问题和工程实际问题.通过对23个标准测试函数的仿真实验,并和已有算法的比较,结果表明本文提出的混合遗传算法是非常有效的.     

A novel local exploitation scheme for conditionally breeding real-coded genetic algorithm

Multimedia Tools and Applications - Conditionally breeding real-coded genetic algorithm (CGAR) is effective approach for continue domain problems, in which crossover and mutation behaviors are...     

基于遗传算法的自动组卷设计

通过分析遗传算法的原理、步骤和运行参数,结合组卷要求提出自动组卷的模型,并根据遗传算法的主要控制参数建立了自动组卷数学模型,用VB程序实现了基于实数编码的遗传算法自动组卷实例。     

基于自由搜索算法求解单机差异工件批调度问题*

采用自由搜索（free search,FS）算法对单机差异工件批调度问题的制作跨度进行优化。针对该问题的离散优化特征以及自由搜索算法的不足,将自由搜索算法与实数编码遗传算法相结合,在标准FS算法的基础上引入两种杂交算子和精英保留策略,提出混合自由搜索（hybrid free search,HFS）算法。仿真实验结果表明,该算法表现出良好的鲁棒性和收敛性,与标准FS、FFLPT以及BFLPT算法相比,HFS算法提高了寻优精度。     

一种结合Tabu 搜索的非线性遗传算法研究

利用Tabu搜索的强大局部搜索性能,提出一种新的非线性遗传算法．该方法将Tabu搜索技术内嵌于遗传算子中,构造了基于Tabu搜索的非线性杂交及变异算子,它能有效地提高算子的局部搜索能力,通过实例仿真证明了该算法的有效性;同时,以“平均截止代数”和“平均截止代数分布熵”作为评价指标,对该方法的优化效率进行研究,定量评价了该方法的优化效率,通过与实数遗传算法进行比较,说明了该方法的优化效率高于实数遗传算法．