An algorithm for ranking assignments using reoptimization

We consider the problem of ranking assignments according to cost in the classical linear assignment problem. An algorithm partitioning the set of possible assignments, as suggested by Murty, is presented where, for each partition, the optimal assignment is calculated using a new reoptimization technique. Its computational performance is compared with all available implementations of algorithms with the same time complexity. The results are encouraging.     

An evolutionary algorithm for resource-constrained projectscheduling

The single-mode, single-project, resource-constrained project-scheduling problem is solved by an evolutionary algorithm. The design of this algorithm is presented. Results of a computational study on two sets of benchmark problems, the first consisting of 330 problem instances and the second 2040, are presented. These results show that the proposed algorithm is effective in terms of the number of times it achieves both the best-known solutions and the average error with respect to these solutions, particularly given that the best-known solutions have been compiled from various sources, using a variety of algorithms. Moreover, the computation time requirements are quite modest     

An evolutionary algorithm for abductive reasoning

Abductive reasoning (or abduction) is the process of inferring hypotheses from observed data using a certain ‘knowledge’ encoded in the form of inference rules (or causal relations). Many important kinds of intellectual tasks, including medical diagnosis, fault diagnosis, scientific discovery, legal reasoning, and natural language understanding have been characterised as abduction. Unfortunately, abduction is 𝒩𝒫-hard. Genetic algorithms and biologically motivated computational paradigms inspired by the natural evolution turned out to be efficient in solving many hard problems while other existing approaches failed to solve in general. In this article, we present a genetic algorithm called HAKIM, for solving abduction problems. We encode an explanation in a chromosome-like structure, where every gene models a possible single hypothesis. Thereafter, we develop a fitness function that characterises the overall ‘quality’ of a chromosome representing an explanation; and then use standard genetic operators to compute a set of hypotheses that best explains the observed data. Simulation results on large-scale medical problems reveal the good performance of our model HAKIM.     

An evolutionary algorithm for robust regression

A drawback of robust statistical techniques is the increased computational effort often needed as compared to non-robust methods. Particularly, robust estimators possessing the exact fit property are NP-hard to compute. This means that—under the widely believed assumption that the computational complexity classes NP and P are not equal—there is no hope to compute exact solutions for large high dimensional data sets. To tackle this problem, search heuristics are used to compute NP-hard estimators in high dimensions. A new evolutionary algorithm that is applicable to different robust estimators is presented. Further, variants of this evolutionary algorithm for selected estimators—most prominently least trimmed squares and least median of squares—are introduced and shown to outperform existing popular search heuristics in difficult data situations. The results increase the applicability of robust methods and underline the usefulness of evolutionary algorithms for computational statistics.     

Stochastic ranking for constrained evolutionary optimization

Penalty functions are often used in constrained optimization. However, it is very difficult to strike the right balance between objective and penalty functions. This paper introduces a novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, and presents a new view on penalty function methods in terms of the dominance of penalty and objective functions. Some of the pitfalls of naive penalty methods are discussed in these terms. The new ranking method is tested using a (μ, λ) evolution strategy on 13 benchmark problems. Our results show that suitable ranking alone (i.e., selection), without the introduction of complicated and specialized variation operators, is capable of improving the search performance significantly     

An improved model-based evolutionary algorithm for multi-objective optimization

The basic idea in the estimation of distribution algorithms is the replacement of heuristic operators with machine learning models such as regression models, clustering models, or classification models. So, recently, the model-based evolutionary algorithms (MBEAs) have been suggested in three groups: The estimation of distribution algorithms (EDAs), surrogate assisted evolutionary algorithms, and the inversed models to map from the objective space to the decision space. In this article, a new approach, based on an inversed model of Gaussian process and random forest framework, is proposed. The main idea is applying the process of random forest variable importance with a random grouping that determines some of the best assignment of decision variables to objective functions in order to form a Gaussian process in inverse models that maps to decision space the rich solutions which are discovered from objective space. Then these inverse models through sampling the objective space generate offspring. The proposed algorithm has been tested on the benchmark test suite for evolutionary algorithms (modified Deb K, Thiele L, Laumanns M, Zitzler E (DTLZ), and Walking Fish Group (WFG)) and indicates that the proposed method is a competitive and promising approach.     

An efficient dynamical evolutionary algorithm for global optimization

In this paper, we introduce a new dynamical evolutionary algorithm (DEA) that aims to find the global optimum and give the theoretical explanation from statistical mechanics. The algorithm has been evaluated numerically using a wide set of test functions which are nonlinear, multimodal and multidimensional. The numerical results show that it is possible to obtain global optimum or more accurate solutions than other methods for the investigated hard problems.     

An organizational evolutionary algorithm for numerical optimization.

Taking inspiration from the interacting process among organizations in human societies, this correspondence designs a kind of structured population and corresponding evolutionary operators to form a novel algorithm, Organizational Evolutionary Algorithm (OEA), for solving both unconstrained and constrained optimization problems. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate the interaction among organizations. In experiments, 15 unconstrained functions, 13 constrained functions, and 4 engineering design problems are used to validate the performance of OEA, and thorough comparisons are made between the OEA and the existing approaches. The results show that the OEA obtains good performances in both the solution quality and the computational cost. Moreover, for the constrained problems, the good performances are obtained by only incorporating two simple constraints handling techniques into the OEA. Furthermore, systematic analyses have been made on all parameters of the OEA. The results show that the OEA is quite robust and easy to use.     

An efficient evolutionary algorithm for accurate polygonal approximation

An optimization problem for polygonal approximation of 2-D shapes is investigated in this paper. The optimization problem for a digital contour of N points with the approximating polygon of K vertices has a search space of C(N, K) instances, i.e., the number of ways of choosing K vertices out of N points. A genetic-algorithm-based method has been proposed for determining the optimal polygons of digital curves, and its performance is better than that of several existing methods for the polygonal approximation problems. This paper proposes an efficient evolutionary algorithm (EEA) with a novel orthogonal array crossover for obtaining the optimal solution to the polygonal approximation problem. It is shown empirically that the proposed EEA outperforms the existing genetic-algorithm-based method under the same cost conditions in terms of the quality of the best solution, average solution, variance of solutions, and the convergence speed, especially in solving large polygonal approximation problems.     

基于估值不确定度排序顺序均值采样的昂贵高维多目标进化算法

模型管理,特别是训练样本的选择和填充采样准则,是影响昂贵多目标优化算法求解性能的重要因素.为此,选择样本库中具有较好目标函数值的若干个体作为样本训练目标函数的代理模型,使用基于参考向量的进化算法搜索模型的最优解集,并提出一种基于个体目标函数估值不确定度排序顺序均值的采样策略,从该最优解集中选择两个个体进行真实的目标函数评价.为了验证算法的有效性,将所提出算法在DTLZ和WFG多目标优化测试问题和两个实际工程优化问题上进行测试,并与其他5种优秀的同类型算法进行结果对比.实验结果表明,所提出算法在求解昂贵高维多目标优化问题上是有效的.     

基于精英集的多目标差分进化聚类算法

聚类数的确定在聚类分析中是一个基本却具有挑战性的问题.一方面,最佳聚类数根据不同的评价标准、用户偏好或需求可能不一致,因此将不同聚类数的聚类结果呈现给用户作参考是有意义的.另一方面,增加聚类数虽会使聚类结果更加紧致,却会削弱不同类之间的分离性,所以选择合适的聚类数是一个在最小化聚类数与最大化类内紧致性或类间分离性之间取...     

An evolutionary algorithm for interval solid transportation problems

The Solid Transportation Problem arises when bounds are given on three item properties. Usually, these properties are source, destination and mode of transport (conveyance), and may be given in an interval way. This paper deals with solid transportation problems in which the data in the constraint set are expressed in an interval form, i.e. when sources, destinations and conveyances have interval values instead of point values. An arbitrary linear or nonlinear objective function is also considered. To solve the problem, an Evolutionary Algorithm which extends and generalizes other approaches considering only point values, is proposed.     

An evolutionary algorithm for large traveling salesman problems

This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each other's disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10,000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.     

An adaptive learning automata-based ranking function discovery algorithm

Due to the massive amount of heterogeneous information on the web, insufficient and vague user queries, and use of the same query by different users for different aims, the information retrieval process deals with a huge amount of uncertainty and doubt. Under such circumstances, designing an efficient retrieval function and ranking algorithm by which the most relevant results are provided is of the greatest importance. In this paper, a learning automata-based ranking function discovery algorithm in which different sources of information are combined is proposed. In this method, the learning automaton is used to adjust the portion of the final ranking that is assigned to each source of evidence based on the user feedback. All sources of information are first given the same importance. The proportion of a given source increases, if the documents provided by this source are reviewed by the user and decreases otherwise. As the proposed algorithm proceeds, the probability of appearance of each source in the final ranking gets proportional to its relevance to the user queries. Several simulation experiments are conducted on well-known data collections and query types to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing methods in terms of precision at position n, mean average precision, and normalized discount cumulative gain.     

一种基于演化算法的BP改进算法

本文在对BP神经网络算法分析的基础上,提出一种基于演化算法的BP改进算法(EBP)。该算法将演化算法运用到BP算法学习率的求解中,从而达到学习率的自适应、自组织的目的。实验结果表明,使用EBP算法进行求解函数逼近、优化和建模等BP神经网络应用问题,都要比传统的BP算法具有更好的精确度和收敛速度,并且能够克服传统BP算法易陷入局部最优解、学习过程出现震荡等缺点。     

An algorithm for non-linear space—Time nuclear reactor control

A novel multivariable control algorithm for non-linear space-time nuclear reactor dynamics is proposed in this paper. The multivariable control algorithm is based on a mathematical model of the nuclear reactor which includes: a single energy group of neutrons, delayed neutron precursors, iodine, xenon and thermal-hydraulic feedback. The multivariable control algorithm is composed of non-linear time-varying feedforward and feedback control signals, a reference model of the nuclear reactor and a dynamic observer. The non-linear proportional plus integral feedback controller forces the nuclear reactor to follow the response of the reference model. The dynamic observer estimates the unmeasurable state variables. The feedforward and feedback control signals are determined in a novel approach by specifying the form of the closed-loop response of the neutron density variables. By virtue of the multivariable control algorithm the closed-loop differential equations are linear and time-varying. A linear stability analysis for base-load and load-cycle operation indicates that the closed-loop system is stable provided that the thermal-hydraulic subsystem is inherently stable. The simulated dynamic response indicates that the multivariable control algorithm provides excellent response characteristics.     

An evolutionary algorithm for fractal coding of binary images

An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images. As the number of maps of the IFS that encodes an image cannot be known in advance, a variable-length genotype is used to represent candidate solutions, Accordingly, feasibility conditions of the maps are introduced, and special genetic operators that maintain and control their feasibility are defined, In addition, several similarity measures are used to define different fitness functions for experimentation. The performance of the proposed methods is tested on a set of binary images, and experimental results are reported     

EvoArch: An evolutionary algorithm for architectural layout design

The architectural layout design problem, which is concerned with the finding of the best adjacencies between functional spaces among many possible ones under given constraints, can be formulated as a combinatorial optimization problem and can be solved with an Evolutionary Algorithm (EA). We present functional spaces and their adjacencies in form of graphs and propose an EA called EvoArch that works with a graph-encoding scheme. EvoArch encodes topological configuration in the adjacency matrices of the graphs that they represent and its reproduction operators operate on these adjacency matrices. In order to explore the large search space of graph topologies, these reproduction operators are designed to be unbiased so that all nodes in a graph have equal chances of being selected to be swapped or mutated. To evaluate the fitness of a graph, EvoArch makes use of a fitness function that takes into consideration preferences for adjacencies between different functional spaces, budget and other design constraints. By means of different experiments, we show that EvoArch can be a very useful tool for architectural layout design tasks.     

An improved evolutionary algorithm for handling many-objective optimization problems

It has been shown that the multi-objective evolutionary algorithms (MOEAs) act poorly in solving many-objective optimization problems which include more than three objectives. The research emphasis, in recent years, has been put into improving the MOEAs to enable them to solve many-objective optimization problems efficiently. In this paper, we propose a new composite fitness evaluation function, in a novel way, to select quality solutions from the objective space of a many-objective optimization problem. Using this composite function, we develop a new algorithm on a well-known NSGA-II and call it FR-NSGA-II, a fast reference point based NSGA-II. The algorithm is evaluated for producing quality solutions measured in terms of proximity, diversity and computational time. The working logic of the algorithm is explained using a bi-objective linear programming problem. Then we test the algorithm using experiments with benchmark problems from DTLZ family. We also compare FR-NSGA-II with four competitive algorithms from the extant literature to show that FR-NSGA-II will produce quality solutions even if the number of objectives is as high as 20.