A novel genetic algorithm with different structure selection for circuit design optimization

In the traditional GA, the tournament selection for crossover and mutation is based on the fitness of individuals. This can make convergence easy, but some useful genes may be lost. In selection, as well as fitness, we consider the different structure of each individual compared with an elite one. Some individuals are selected with many different structures, and then crossover and mutation are performed from these to generate new individuals. In this way, the GA can increase diversification into search spaces so that it can find a better solution. One promising application of GA is evolvable hardware (EHW), which is a new research field to synthesize an optimal circuit. We propose an optimal circuit design by using a GA with a different structure selection (GAdss), and with a fitness function composed of circuit complexity, power, and signal delay. Its effectiveness is shown by simulations. From the results, we can see that the best elite fitness, the average fitness value of correct circuits, and the number of correct circuits with GAdss are better than with GA. The best case of optimal circuits generated by GAdss is 8.1% better in evaluation value than that by traditional GA.     

A genetic algorithm for a bicriteria supplier selection problem

In this paper, we discuss the problem of selecting suppliers for an organisation, where a number of suppliers have made price offers for supply of items, but have limited capacity. Selecting the cheapest combination of suppliers is a straightforward matter, but purchasers often have a dual goal of lowering the number of suppliers they deal with. This second goal makes this issue a bicriteria problem – minimisation of cost and minimisation of the number of suppliers. We present a mixed integer programming (MIP) model for this scenario. Quality and delivery performance are modelled as constraints. Smaller instances of this model may be solved using an MIP solver, but large instances will require a heuristic. We present a multi-population genetic algorithm for generating Pareto-optimal solutions of the problem. The performance of this algorithm is compared against MIP solutions and Monte Carlo solutions.     

基于量子遗传算法的特征选择算法

特征选择是模式识别和机器学习等领域中重要而困难的研究课题.提出一种最优特征子集评价准则和实现特征选择的一种新量子遗传算法(NQGA).NQGA采用量子门旋转角更新新方法和增强算法寻优能力及防止早熟收敛的移民和灾变策略.定性分析了NQGA的高效性.典型复杂函数测试和雷达辐射源信号特征选择的应用表明,NQGA寻优能力强、收敛速度快和能有效防止早熟现象.采用提出的准则函数和搜索策略实现特征选择,大大降低了特征维数,获得了更高的正确识别率.     

Hybrid genetic algorithm for dual selection

In this paper, a hybrid genetic approach is proposed to solve the problem of designing a subdatabase of the original one with the highest classification performances, the lowest number of features and the highest number of patterns. The method can simultaneously treat the double problem of editing instance patterns and selecting features as a single optimization problem, and therefore aims at providing a better level of information. The search is optimized by dividing the algorithm into self-controlled phases managed by a combination of pure genetic process and dedicated local approaches. Different heuristics such as an adapted chromosome structure and evolutionary memory are introduced to promote diversity and elitism in the genetic population. They particularly facilitate the resolution of real applications in the chemometric field presenting databases with large feature sizes and medium cardinalities. The study focuses on the double objective of enhancing the reliability of results while reducing the time consumed by combining genetic exploration and a local approach in such a way that excessive computational CPU costs are avoided. The usefulness of the method is demonstrated with artificial and real data and its performance is compared to other approaches.

一种基于选择的多目标遗传算法

利用遗传算法对多目标问题进行优化时,得到的Pareto解在目标空间中为均匀分布,但有时希望能在目标空间的某个区域内产生比较稠密的Pareto解.针对这一问题,提出一种基于选择的多目标遗传算法,该算法在遗传操作过程中通过选择来引导寻优方向,最终得到满意的解.     

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

遗传算法的相似性配对方式

提出了一种新的遗传算法配对方式,并计算了配对概率。以这种配对方式为基础,对一个极大值问题作了计算机模拟。结果表明,这种配对方法从生物学角度来说,更符合生物世界的真实配对方式。而从探索最优解的角度来说,这种配对方式有助于优良基因结构的保留。因此这种配对方式可加快计算的收敛速度。     

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.     

求解多目标投资组合优化模型的遗传算法

针对考虑最小交易量、交易费用,以及单项目最大投资上限约束的多目标投资组合模型,对目标函数添加惩罚函数项来处理约束条件的方法.本文通过对交叉算子、变异算子的改进,设计了一种遗传算法进行求解.实验算例表明,该算法是有效的.     

A new and fast rival genetic algorithm for feature selection

Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in feature selection tasks. However, GA suffers from the hyperparameter setting, high computational complexity, and the randomness of selection operation. Therefore, we propose a new rival genetic algorithm, as well as a fast version of rival genetic algorithm, to enhance the performance of GA in feature selection. The proposed approaches utilize the competition strategy that combines the new selection and crossover schemes, which aim to improve the global search capability. Moreover, a dynamic mutation rate is proposed to enhance the search behaviour of the algorithm in the mutation process. The proposed approaches are validated on 23 benchmark datasets collected from the UCI machine learning repository and Arizona State University. In comparison with other competitors, proposed approach can provide highly competing results and overtake other algorithms in feature selection.

     

Markov blanket-embedded genetic algorithm for gene selection

Microarray technologies enable quantitative simultaneous monitoring of expression levels for thousands of genes under various experimental conditions. This new technology has provided a new way of biological classification on a genome-wide scale. However, predictive accuracy is affected by the presence of thousands of genes many of which are unnecessary from the classification point of view. So, a key issue of microarray data classification is to identify the smallest possible set of genes that can achieve good predictive accuracy. In this study, we propose a novel Markov blanket-embedded genetic algorithm (MBEGA) for gene selection problem. In particular, the embedded Markov blanket-based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on synthetic and microarray benchmark datasets suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. A detailed comparative study with other methods from each of filter, wrapper, and standard GA shows that MBEGA gives a best compromise among all four evaluation criteria, i.e., classification accuracy, number of selected genes, computational cost, and robustness.     

遗传算法选择策略比较

以遗传算法中的轮盘赌选择策略和锦标赛选择策略作为研究对象,通过在13个基准测试函数上的测试,对不同选择策略的性能进行了比较和分析.实验结果表明,锦标赛选择策略比轮盘赌选择策略具有更好的通用性,而且性能更优.在锦标赛选择策略中,组规模为种群规模的60%至80%的锦标赛选择策略效果较好.该实验结果为设计更加合理高效的选择策略提供了有用的参考.     

基于遗传算法的图像特征选择

针对模式识别时,提取的特征参数量大而又有冗余的现象,提出了基于遗传算法的特征选择方法。介绍了遗传算法的基本原理,阐述并设计了适应度函数和遗传算子。仿真实验表明,该方法在求解的效率和解的质量方面都达到了令人满意的效果。     

Genetic drift in genetic algorithm selection schemes

A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm (1991), and (μ+λ) evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically     

遗传算法中自适应的比例选择策略

基于适应度比例的选择策略是遗传算法的基本选择方法,但采用该策略易出现未成熟收敛和随机漫游现象。通过实验分析了两种现象的成因,提出采用自适应的比例选择策略来依据种群性状的改变而动态地调整选择压力,进而调整算法求精和求泛能力的平衡。分析和对比实验证实,新的选择策略可有效克服未成熟收敛和随机漫游现象。     

A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB

Facing current environment full of a variety of small quantity customized requests, enterprises must provide diversified products for speedy and effective responses to customers’ requests. Among multiple plans of product, both assembly sequence planning (ASP) and assembly line balance (ALB) must be taken into consideration for the selection of optimal product plan because assembly sequence and assembly line balance have significant impact on production efficiency. Considering different setup times among different assembly tasks, this issue is an NP-hard problem which cannot be easily solved by general method. In this study the multi-objective optimization mathematical model for the selection of product plan integrating ASP and ALB has been established. Introduced cases will be solved by the established model connecting to database statistics. The results show that the proposed Guided-modified weighted Pareto-based multi-objective genetic algorithm (G-WPMOGA) can effectively solve this difficult problem. The results of comparison among three different kinds of hybrid algorithms show that in terms of the issues of ASP and ALB for multiple plans, G-WPMOGA shows better problem-solving capability for four-objective optimization.     

fGAAM: A fast and resizable genetic algorithm with aggressive mutation for feature selection

Pattern Analysis and Applications - The paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM) called fGAAM (fast GAAM) that significantly decreases the time...     

A feature selection bayesian approach for extracting classification rules with a clustering genetic algorithm

This work describes a method that combines a Bayesian feature selection approach with a clustering genetic algorithm to get classification rules in data-mining applications. A Bayesian network is generated from a data set and the Markov blanket of the class variable is applied to the feature subset selection task. The general rule extraction method is simple and consists of employing the clustering process in the examples of each class separately. In this way, clusters of similar examples are found for each class. These clusters can be viewed as subclasses and can, consequently, be modeled into logical rules. In this context, the problem of finding the optimal number of classification rules can be viewed as the problem of finding the best number of clusters. The Clustering Genetic Algorithm can find the best clustering in a data set, according to the Average Silhouette Width criterion, and it was applied to extract classification rules. The proposed methodology is illustrated by means of simulations in three data sets that are benchmarks for data-mining methods--Wisconsin Breast Cancer, Mushroom, and Congressional Voting Records. The rules extracted with all the attributes are compared to those extracted with the features belonging to the Markov blanket and the obtained results show that the proposed method is very promising.