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.     

Natural Encoding for Evolutionary Supervised Learning

Some of the most influential factors in the quality of the solutions found by an evolutionary algorithm (EA) are a correct coding of the search space and an appropriate evaluation function of the potential solutions. EAs are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators. Our approach, called "natural coding," uses one gene per feature in the dataset (continuous or discrete). The examples from the datasets are also encoded into the search space, where the genetic population evolves, and therefore the evaluation process is improved substantially. Genetic operators for the natural coding are formally defined as algebraic expressions. Experiments with several datasets from the University of California at Irvine (UCI) machine learning repository show that as the genetic operators are better guided through the search space, the number of rules decreases considerably while maintaining the accuracy, similar to that of hybrid coding, which joins the well-known binary and real representations to encode discrete and continuous attributes, respectively. The computational cost associated with the natural coding is also reduced with regard to the hybrid representation. Our algorithm, HlDER*, has been statistically tested against C4.5 and C4.5 Rules, and performed well. The knowledge models obtained are simpler, with very few decision rules, and therefore easier to understand, which is an advantage in many domains. The experiments with high-dimensional datasets showed the same good behavior, maintaining the quality of the knowledge model with respect to prediction accuracy.     

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

The continuous growth of computation power requirement has provoked computational Grids, in order to resolve large scale problems. Job scheduling is a very important mechanism and a better scheduling scheme can greatly improve the efficiency of Grid computing. A lot of algorithms have been proposed to address the job scheduling problem. Unfortunately, most of them largely ignore the security risks involved in executing jobs in such an unreliable environment as Grid. This is known as security problem and it is a main hurdle to make the job scheduling secure, reliable and fault-tolerant. In this paper, we present a Genetic Algorithm with multi-criteria approach, in terms of job completion time and security risks. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, proposing the Accelerated Genetic Algorithm. We also present the Accelerated Genetic Algorithm with Overhead which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristic algorithms. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.     

遗传算法的研究与应用

根据遗传算法的一些基本概念以度遗传算法的操作流程。对遗传算法的繁殖算子从数学上给出定义，刻划了繁殖算子的本质。遗传算法是一种基于概率意义上的随机搜索算法。但它是从空间上的一组点而不是一个点出发。因此遗传算法的搜索能力比其他随机搜索算法更强，可以找到全局范围内的最优解。但是。应该注意在遗传算法的应用中，要避免其过早的收敛，防止陷入局部最优解。     

智能规划中基于遗传算法的动作模型学习

在动作间的状态未知条件下,利用遗传算法,从不完整的领域描述和规划实例中学习动作模型,并且设计了AMLS-GA(Action Model Learning System Based on Genetic Algorithm)系统来具体实现这一思想.作者为每一个动作构建一个可能谓词集,这个谓词集覆盖了动作前提表、增加表和删除表中的所有谓词.采用二进制编码的方式,把动作模型编码成GA搜索空间中的一个假设,学习过程是在标准的遗传算法框架下进行的.把学习结果的正确性定义为尽可能多的解释规划实例,并且通过实验的方法对比学习到的模型与专家预定义模型之间的差别.实验结果表明,算法能在较短的时间内,学习到一个逼近专家描述的动作模型.     

基于线杂交和面变异的遗传算法DGA

遗传算法由于其并行性和对全局信息的有效利用能力在化学和化工界得到越来越广泛的应用。但经典的跗算法在着一些缺点,如优化速度慢、空间搜索不均匀,搜索比较盲目等＾〖１〗。针对这些缺点,我们提出了结合均匀设计、有方向的搜索和遗传算法的确定性遗传算法ＤＧＡ,并用１８个经典测试函数和３个非线性规划问题对ＤＧＡ进行了测试。     

A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design

Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.     

Application of variable search space genetic algorithms to fine gain tuning of model-based robotic servo controller

In this paper, genetic algorithms with a variable search space function are proposed for fine gain tuning of a resolved acceleration controller which is one of model-based robotic servo controllers. Genetic algorithms proposed in this paper have a variable search space function which is activated if the optimal solution is not updated for a fixed number of generations. The function is terminated when the optimal solution is updated, or if the optimal solution is not updated within certain generations. This proposed method is evaluated through a trajectory following control problem in simulation. Simulations for sine curve trajectories are conducted using the dynamic model of the PUMA560 manipulator. The result shows the improvement of optimal solution and its convergence.     

The application of genetic algorithms to operation sequencing for use in computer-aided process planning

Operation sequencing has long been a difficult problem in process planning. As part complexity increases, the number of potential solutions increases exponentially. This paper presents an approach to operation sequence coding that permits the application of genetic algorithms for quickly determining optimal, or near-optimal, operation sequences for parts of varying complexity. This approach improves on existing techniques by utilizing common sequencing constraints to guide the coding process resulting in a further reduction in the size of the solution search space. These improvements permit the determination of near-optimal operation sequences for complex parts within a time frame necessary for real-time dynamic planning. Application of this strategy is illustrated using three parts of varying complexity as well as comparing the genetic algorithm's performance using the improved constrained coding strategy with that of an unconstrained strategy.