Stochastic sketching: a new method for global optimization

 In this paper, we propose Stochastic sketching for global optimization based on a simulation of human behaviour. Stochastic sketching tries to do things simply in the human way without too much interpretation instead of modeling the thought and strategies of human beings and applying an artificial model to problems. We introduce and discuss concepts and components essential to stochastic sketching in detail, including sampling guide, zooming controller, sketching model, precision threshold, and satisfaction probability. Experimental results of stochastic sketching on several test functions and a set of recommended parameter settings are given, as well as preliminary comparisons between stochastic sketching and related evolutionary algorithms including evolution strategies, evolutionary programming, and genetic algorithms.     

A single-point mutation evolutionary programming

In this paper, we propose an improved evolutionary programming based on single-point mutation, which is named Single-Point Mutation Evolutionary Programming (SPMEP). The distinctions between SPMEP and the classical evolutionary programming (EP) are the single-point mutation for each solution in each iteration and the fixed mutation scheme for deviation η. Simulation results show that SPMEP is obviously superior to the classical EP, fast EP and generalized EP for multimodal and high-dimensional functions.     

Controlled mutation in evolutionary structural optimization

The paper deals with minimum weight design applying Evolutionary Strategy (ES), improved by controlled mutation. Applied selection and crossover are typical for ES. Mutations, however, are controlled by state variables, in this case by stresses. After crossover, from each population, a voted number of chromosomes are inspected from the point of view of stresses occurring in structural members (genes). Changes of cross-sectional areas are introduced depending on the minimum, or maximum stresses. 124 runs of the algorithm were performed on a bench-mark problem, with known optimum solution. The number of exact solutions, applying controlled mutation, was four times larger than in the case of a simple ES algorithm. Received January 27, 2000     

Structured population-based incremental learning

 We investigate a recently developed abstraction of genetic algorithms (GAs) in which a population of GAs in any generation is represented by a single vector whose elements are the probabilities of the corresponding bit positions being equivalent to 1. The process of evolution is represented by learning the elements of the probability vector; the method is clearly linked to the artificial neural network (ANN) method of competitive learning. We use techniques from ANNs to extend the applicability of the method to non-static problems, to multi-objective criteria, to multi-modal problems and to creating an order on a set of sub-populations.     

A directional mutation operator for differential evolution algorithms

Differential evolution (DE) is widely studied in the past decade. In its mutation operator, the random variations are derived from the difference of two randomly selected different individuals. Difference vector plays an important role in evolution. It is observed that the best fitness found so far by DE cannot be improved in every generation. In this article, a directional mutation operator is proposed. It attempts to recognize good variation directions and increase the number of generations having fitness improvement. The idea is to construct a pool of difference vectors calculated when fitness is improved at a generation. The difference vector pool will guide the mutation search in the next generation once only. The directional mutation operator can be applied into any DE mutation strategy. The purpose is to speed up the convergence of DE and improve its performance. The proposed method is evaluated experimentally on CEC 2005 test set with dimension 30 and on CEC 2008 test set with dimensions 100 and 1000. It is demonstrated that the proposed method can result in a larger number of generations having fitness improvement than classic DE. It is combined with eleven DE algorithms as examples of how to combine with other algorithms. After its incorporation, the performance of most of these DE algorithms is significantly improved. Moreover, simulation results show that the directional mutation operator is helpful for balancing the exploration and exploitation capacity of the tested DE algorithms. Furthermore, the directional mutation operator modifications can save computational time compared to the original algorithms. The proposed approach is compared with the proximity based mutation operator as both are claimed to be applicable to any DE mutation strategy. The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family. Finally, the applications of two real world engineering optimization problems verify the usefulness of the proposed method.     

Adaptive mutation in genetic algorithms

 In Genetic Algorithms mutation probability is usually assigned a constant value, therefore all chromosome have the same likelihood of mutation irrespective of their fitness. It is shown in this paper that making mutation a function of fitness produces a more efficient search. This function is such that the least significant bits are more likely to be mutated in high-fitness chromosomes, thus improving their accuracy, whereas low-fitness chromosomes have an increased probability of mutation, enhancing their role in the search. In this way, the chance of disrupting a high-fitness chromosome is decreased and the exploratory role of low-fitness chromosomes is best exploited. The implications of this new mutation scheme are assessed with the aid of numerical examples.     

带变异算子的双种群粒子群优化算法

提出一种带变异算子的双种群粒子群算法,搜索在两个不同的子群中并行运行,分别使用不同的惯性权值,使得种群在全局和局部都有较好的搜索能力.通过子群重组实现种群间的信息交换.在算法中引入变异算子,产生局部最优解的邻域点,帮助惰性粒子逃离束缚,寻得更优解.对经典函数的测试结果表明,改进的算法在收敛速度和精度上有更好的性能.     

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

 In the present paper a special bit-masking oriented data structure for an improved implementation of crossover and mutation operators in genetic algorithms is shown. The developed data structure performs evolutionary operators in two separate steps: crossover and mutation mask fill and a special boolean based function application. Both phases are optimized to reach a more efficient, fast and flexible genetic reproduction than standard implementations. The method has been powered adding a multi-layered, bit-masking oriented data structure and a boolean operation based control mixer, allowing special blended crossover operators obtained by superposition of the standard ones. Several examples of crossover schemes produced by these extended controls are presented. In addition, a special purpose crossover scheme, capable to process at the same time two distinct groups of design variables with separate crossover schemes is shown, in order to improve efficiency and convergence speed of some discrete/continuous optimization problems. Finally, to highlight further capabilities of the bit-masking approach, a special single-step version of an evolutionary direction operator is also illustrated.     

Development of CFRP racing motorcycle rims using a heuristic evolutionary algorithm approach

The scope of this paper is the application of evolutionary optimization methods to the development of composite fibre reinforced plastics (CFRP) racing motorcycle rims. The mass and the moment of inertia of a front and a rear CFRP rim are minimized subject to manufacturing, strength, and stiffness constraints. The stacking sequence of the composite laminates is optimized by applying a sophisticated parameterization concept making an excellent compromise between a huge variety of structure properties and a reasonable number of optimization parameters. The mechanical properties are simulated using the finite element analysis package ANSYS . Resulting displacement and Tsai–Wu index values are combined with the mass of the rim in order to assign a fitness value to each different design solution. The smart formulation of the fitness function allows the exploration of solutions close to the required strength and stiffness properties. The proprietary software DynOPS is utilized as an optimization engine. It links an evolutionary algorithm to arbitrary simulation programs and controls the entire optimization process. The sophisticated parameterization concept, together with the fitness function formulation, are the basis for the development of CFRP motorcycle rims decisively lighter than state-of-the-art magnesium alloy rims.     

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

 Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.     

求解多目标优化问题的分级变异量子进化算法

分析量子进化算法和免疫算子的特点,提出一种分级变异的量子进化算法,用于求解多目标优化问题,算法主要基于两个策略:首先,利用快速非受控排序和密度距离计算种群抗原-抗体的亲和度;然后,基于亲和度排序将个体进行分级,最优分级中的个体作为算法中的最优个体,大部分实施量子旋转更新和免疫操作,而剩余分级中的个体实施免疫交叉操作以获得新的个体补充种群,求解多目标0/1背包问题的实验结果表明了该算法的有效性.     

Improving the design of sequences for DNA computing: A multiobjective evolutionary approach

Designing oligonucleotide strands that selectively hybridize to reduce undesired reactions is a critical step for successful DNA computing. To accomplish this, DNA molecules must be restricted to a wide window of thermodynamical and logical conditions, which in turn facilitate and control the algorithmic processes implemented by chemical reactions. In this paper, we propose a multiobjective evolutionary algorithm for DNA sequence design that, unlike preceding evolutionary approaches, uses a matrix-based chromosome as encoding strategy. Computational results show that a matrix-based GA along with its specific genetic operators may improve the performance for DNA sequence optimization compared to previous methods.     

一种新的进化计算算法模型--种群竞争消亡算法

为克服进化计算自身的早熟收敛缺陷,受自然界和人类社会进化现象的启发,文中研究得到了一种新的进化计算算法模型——种群竞争消亡算法。本文将该模型应用于温室作物生长模型的参数优化,并将试验结果与基本进化计算相比较,结果说明种群竞争消亡算法在稳定性和收敛性上确实比基本进化计算优越。     

Integrating adaptive mutations and family competition into genetic algorithms as function optimizer

 In this paper, we propose a robust evolutionary algorithm, called adaptive mutations genetic algorithm, for function optimization problems. Our main contribution is robustly optimizing problems whose number of variables from 2 to 200. In order to have a fair comparison, we propose the criteria for constructing a testing bed and for classifying these problems into different complexity degrees. The proposed approach, based on the family competition and multiple adaptive rules, successfully integrates the decreasing-based Gaussian mutation and self-adaptive Cauchy mutation to balance the exploitation and exploration. It is implemented and applied to widely used test functions and several nonseparable multimodal functions. Experimental results indicate that our approach is more robust than ten evolutionary algorithms.     

Global web server load balancing using evolutionary computational techniques

 As user load on web servers becomes more globalised, duplicated regional mirroring is being seen as an increasingly expensive solution to meeting regional peak demand. Alternative solutions are being explored by looking at dynamic load balancing using distributed intelligent middleware to re-route traffic from busy regions to quieter ones as global load patterns change over a 24 h cycle. The techniques used can also be employed under fault and planned maintenance conditions. One such solution, providing the load balancing via reconfigurable dynamic proxy servers, is seen as `unobtrusive' in that it works with standard web browsers and web server technology. The technique employs an evolutionary algorithm to perform combinatorial optimisation against a dynamic performance predicting model. This paper describes this solution, focussing on issues such as algorithm tuning, scalabilty and reliability. A prototype system is currently being trialled within the Systems Integration Department at British Telecommunications Research Labs, Adastral Park and is the subject of several BT held patents.     

Fuzzy evolutionary computing

 This paper elaborates on a new paradigm of computing embracing fuzzy sets and evolutionary methods (specially genetic algorithms). We discuss conceptual and algorithmic enhancements to the individual methods. Fuzzy sets are geared toward granular information processing. Evolutionary computing are population-based optimization methods. In this way, as being components of any hybrid structure, they naturally complement each other. The study reveals a number of representative symbiotic links between fuzzy and genetic computing and provides with relevant illustrative examples.     

SID-GA: An evolutionary approach for improving observability and redundancy analysis in structural instrumentation design

In this paper the core of a genetic algorithm designed to define a sensor network for instrumentation design (ID) is presented. The tool has been incorporated into a decision support system (DSS) that assists the engineer during the ID process. The algorithm satisfactorily deals with non-linear mathematical models, and considers four design objectives, namely observability, cost, reliability and redundancy, exhibiting properties that were either never addressed by existing techniques or partially dealt with in the literature. Its performance was tested by carrying out the ID of an ammonia synthesis industrial plant. Results were statistically analysed. A face validity study on the fitness function’s soundness was also assessed by a chemical engineer with insight and expertise in this problem. The technique performed satisfactorily from the point of view of the expert in ID, and therefore it constitutes a significant upgrading for the DSS.     

函数优化的一种高效演化算法

提出了一个求解函数优化问题的高效演化算法,其设计思想由混合选择策略与分类变异簟略构成。该算法使用锦标赛选择、轮盘选择相结合的混合选择策略。变异运算分为三类进行：对最好个体实施模式搜索。对适应值排名靠前的三分之一的个体采用柯西变异,而其它个体使用普通变异算子。针对15个测试函数的实验取得了相当好的效果,实验结果表明该算法不仅收敛速度快．而且所求得的解达到或者以相当高的精度逼近最优解。     

A genetic-based methodology for hybrid electric vehicles sizing

 As private transport concerns, the global challenge of this millennium is the reduction of carbon dioxide emissions from passenger cars by improving fuel economy without sacrificing the vehicle performance. Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can improve effectively the vehicle performance and fuel economy, reducing at the same time the effects of the use of private cars on the air quality of the cities. These advantages can be achieved only if the design of the powertrain is inspired to the minimisation of the main figures of merit holding in consideration many general aspects and variables. As supporting methodology in developing this difficult activity, a genetic-based sizing methodology will be presented. It will be aimed to minimise a function objective which takes into account not only technical specifications but also environmental, social, and economic aspects. Some interesting simulation results will be reported to prove the validity of the methodology, which will contribute to a substantial reduction of the pollutant emissions from hybrid electric vehicles.