Distributed, Collaborative Data Analysis from Heterogeneous Sites Using a Scalable Evolutionary Technique

This paper documents an early effort to develop an experimental, collaborative data analysis technique for learning classifiers from a collection of heterogeneous datasets distributed over a network. The proposed technique makes use of a scalable evolutionary algorithm, called the GEMGA to classify datasets. This paper describes the developed technique and the results of the use of this technique through the application of this system for several domains, including distributed fault detection in an electrical power distribution network.     

Supply Estimation Using Coevolutionary Genetic Algorithms in the Spanish Electrical Market

The price of electrical energy in Spain has not been regulated by the government since 1998, but determined by the supply from the generators in a competitive market, the so-called electrical pool. A genetic method for analyzing data from this new market is presented in this paper. The eventual objective is to determine the individual supply curves of the competitive agents. Adopting the point of view of the game theory, different genetic algorithm configurations using coevolutionary and non-coevolutionary strategies combined with scalar and multi-objective fitness are compared. The results obtained are the first step toward solving the induction of the optimal individual strategies into the Spanish electrical market from data in terms of perfect oligopolistic behavior.     

Evolutionary Techniques for Web Caching

Web caching has been proposed as an effective solution to the problems of network traffic and congestion, Web objects access and Web load balancing. This paper presents a model for optimizing Web cache content by applying either a genetic algorithm or an evolutionary programming scheme for Web cache content replacement. Three policies are proposed for each of the genetic algorithm and the evolutionary programming techniques, in relation to objects staleness factors and retrieval rates. A simulation model is developed and long term trace-driven simulation is used to experiment on the proposed techniques. The results indicate that all evolutionary techniques are beneficial to the cache replacement, compared to the conventional replacement applied in most Web cache server. Under an appropriate objective function the genetic algorithm has been proven to be the best of all approaches with respect to cache hit and byte hit ratios.     

Using Genetic Algorithms for Solving Hard Problems in GIS

Genetic algorithms (GAs) are powerful combinatorial optimizers that are able to find close-to-optimal solutions for difficult problems by applying the paradigm of adaptation through Darwinian evolution. We describe a framework for GAs capable of solving certain optimization problems encountered in geographical information systems (GISs). The framework is especially suited for geographical problems since it is able to exploit their geometrical structure with a novel operator called the geometrically local optimizer. Three such problems are presented as case studies: map labeling, generalization while preserving structure, and line simplification. Experiments show that the GAs give good results and are flexible as well.     

一种解函数优化问题的精英子空间演化算法

文献[1]提出了一种有效的求解优化问题的演化算法,算法简单易行,并能在较短时间内找到问题的最优解,在该算法的基础上,文中提出了一种精英子空间演化算法,在算法的杂交过程中,通过采用精英保存策略,增加父体的选择压力来加速算法的收敛,数值试验表明新算法比文献[1]中的算法具有更好的收敛性,能更快地找到优化问题的最优解。     

A Comparison of Static Analysis and Evolutionary Testing for the Verification of Timing Constraints

This paper contrasts two methods to verify timing constraints of real-time applications. The method of static analysis predicts the worst-case and best-case execution times of a task's code by analyzing execution paths and simulating processor characteristics without ever executing the program or requiring the program's input. Evolutionary testing is an iterative testing procedure, which approximates the extreme execution times within several generations. By executing the test object dynamically and measuring the execution times the inputs are guided yielding gradually tighter predictions of the extreme execution times. We examined both approaches on a number of real world examples. The results show that static analysis and evolutionary testing are complementary methods, which together provide upper and lower bounds for both worst-case and best-case execution times.     

数据仓库物化视图选择的混合算法

物化视图是提高数据仓库的查询响应能力以高效支持决策分析的重要手段，但物化视图集选选择是一个复杂问题。结合启发式算法的快速收敛能力和遗传算法的全局优化能力的两层物化视图求解方案提供了物化视图选择问题求解的可行途径。     

求解函数优化问题的一种高效混合演化算法

在郭涛算法的基础上设计出了一种求解函数优化问题的高效混合演化算法。新算法的主要特点有两个:一是引入演化策略中的高斯变异算子,二是引入自适应搜索子空间。高斯变异算子对群体作正态分布微调,防止早熟;引入自适应搜索子空间使群体在演化至接近全局最优解时能自动缩小搜索范围,从而达到加速收敛的目的。测试函数表明,该算法正确高效,求解精度极高,指正了文献[3]中的错误,所求函数全局最小值优于文献[3]记录的最好结果。     

位重要性进化算法

研究了遗传算法中的位重要性和位收敛顺序性,给出了重要位、模式、参数区间和目标函数四者之间的关系,提出了一种新的进化算法——位重要性进化算法（Bit Importance Evolutionary Algorithm,BIEA）.BIEA通过检测组成个体各位的重要性,对于重要位,加快其收敛;对于非重要位,保持其多样性.数据实验表明：BIEA在收敛速度上要优于遗传算法,同时BIEA也可以有效地解决一类遗传算法很难解决的强欺骗性问题.     

Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance

The development of powerful computers and faster input/output devices coupled with the need for storing and analyzing data have resulted in massive databases (of the order of terabytes). Such volumes of data clearly overwhelm more traditional data analysis methods. A new generation of tools and techniques are needed for finding interesting patterns in the data and discovering useful knowledge. In this paper we present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of self-adaptive feature selection together with a wrapper feature selection method based on Hausdorff distance measure.     

基于进化算法的软件测试数据生成的自动化

介绍了进化算法在测试数据生成中的应用思想,分析了在不同的覆盖准则下应用进化算法时适值函数的设计方法,把遗传算法等进化算法应用到测试数据生成中的进化测试技术,克服了局部搜索算法无法为较复杂程序的分支生成测试输入的问题。     

求解模糊机会约束规划的混合智能算法

模糊机会约束规划是一类重要的模糊规划,它广泛地存在于许多领域中,微粒群算法已实现了对其的有效求解,但求解速度仍不能满足大规模模糊机会约束规划问题的求解,为了寻找更为高效的求解模糊机会约束规划的算法,通过采用模糊模拟产生样本训练BP网络以逼近模糊函数,然后应用微粒群算法并以逼近模糊函数的神经网络作为适应值估计及检验解的可行性,从而提出了一种求解模糊机会约束规划的混合智能算法。最后通过仿真结果说明了算法的正确性和有效性。     

基于进化计算的模糊控制规则优化

在改进的遗传算法基础上，提出了模糊控制规则的一种优化方法，模糊规则的优化基于类似最优控制的二次型性能指标，不需要任何被控系统的先验知识，因而适合于一大类复杂非线性系统的控制，仿真结果表明，这种方法是有效的。     

不等式约束的非线性规划混合遗传算法

针对带不等式约束的非线性规划问题,提出了一个混合遗传算法。该算法分为全局探测和局部开采两个阶段,全局探测阶段是通过在有潜力的小生境内嵌入单纯形搜索,快速确定有前景的区域;而局部开采阶段则是在最有前景的区域进行单纯形搜索。该算法增强了局部搜索能力并同时保持种群的多样性,有效地解决了遗传算法的过早收敛和局部搜索能力弱的问题。典型非线性规划算例验证了混合算法的效率、精度和可靠性。     

A Two-Layer Robot Controller Design Using Evolutionary Algorithms

The results obtained by a rule-based proportional, integral, derivative (PID) precompensator controller applied to a two-joint manipulator are discussed. The end effector is made to follow a specified trajectory obtained from the inverse kinematics by an appropriate design of a fuzzy control law. The desired trajectory is determined by the values of the joint variables and the structural kinematics parameters of the manipulator. The performance of the PID controller is exploited here to build a fuzzy precompensator that will enhance the conventional PID and to obtain better performances and results. The fuzzy rule base of the precompensator designed is found by associating two evolutionary algorithms that search for the optimal solution.     

求解模糊Job Shop调度的遗传算法与蚁群算法融合研究

提出一种算法融合策略,解决单一算法求解模糊Job Shop调度问题存在的不足,提高这类问题的求解质量.算法融合策略中,采用遗传算法和蚁群算法进行并行搜索;根据模糊Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,并将基于这种邻域选择方法的禁忌搜索算法作为局部搜索算法,加强了遗传算法和蚁群算法的局部搜索能力.采用算法融合策略的混合优化算法对以13个难的benchmarks问题经模糊化得到实例进行求解,在较短的时间内,得到的平均满意度较并行遗传算法(PGA)提高5.24%、较TSAB算法提高8.40% .采用算法融合策略构造的混合算法具有较强的搜索能力,说明提出的混合搜索策略是有效的.     

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。     

A New Approach to Solving Data Clusterization Problems

A new approach to solving some problems of cluster analysis is proposed, which reduces a multi-dimensional problem to a one-dimensional one.