Early bankruptcy prediction using ENPC

Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

改进的粒子群优化算法

将基本粒子群算法粒子行为基于个体极值点和全局极值点变化为基于个体极值中心,并且按一定概率选择其他粒子的个体极值点,设计了一种新的粒子群优化算法.新算法的学习行为符合自然界生物的学习规律,更有利于粒子发现问题的全局最优解.实验结果表明了算法的有效性.     

一种特征加权FCM算法的图像重建技术研究

针对传统FCM(Fuzzy C-means)算法中初始聚类中心选取的随机性以及对初始值敏感的问题,提出一种基于进化策略的色彩空间加权的FCM聚类算法.通过在RGB(Red Green Blue)色彩空间矢量中设置加权矩阵来补偿各色彩的非均匀性,并采用一种类内最小距离最大的统计聚类算法来初始化聚类中心.实验结果表明,该算法能有效减少颜色量化后的均方差值,保持重建图像的整体层次和局部特征细节,对研究图像处理技术有较强的实际意义.

Abstract：

Aiming at a defect on randomness of the initial clustering center choosing and sensitivity of initial value in tradition FCM(fuzzy C-means) algorithm, a clustering algorithm about FCM of weighted color space based on evolutionary strategy is proposed. By interposing weighted matrix in RGB(Red Green Blue) color space, the color's inhomogeneous is compensated. And by using a statistics clustering algorithm of minimal maximal distance, clustering center is initiated. The experimental results show that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction, and has practical value to the study of the image process technology.     

一种实数编码量子进化算法及其收敛性

基于量子计算理论和进化理论,提出一种新的量子进化算法--基于实数编码的量子进化算法(RQEA).不同于传统进化算法的单点编码和量子进化算法的量子比特编码,该算法以实数矩形区域表示基因,一条染色体携带多个个体信息,利用量子态叠加和相干机理,通过叠加、变异及自学习来完成进化过程,理论分析证明了算法具有全局收敛性,实验结果表明,该算法在函数优化上具有优异的性能.     

基于非结构化控制流的进化测试方法

结构性测试中,进化测试是一种高效的自动生成高质量测试用例的技术.然而,当程序中出现非结构化特征时,在面向节点的测试标准下,其效率极其低下甚至不及随机测试生成方法.在考虑循环体内部出现跳转(goto、retum)语句的情况下,提出了一种适应度计算方法.该方法在结合传统进化测试适应度计算的基础上,充分考虑了循环次数对于进化搜索的影响.实验结果表明,本适应度函数可以很好地引导进化搜索,并以较小的代价生成测试用例.     

基于系统动力学的环境污染演化博弈问题研究

用系统动力学建立环境污染管理问题中政府管理部门与生产排污企业之间的一个混合战略演化博弈模型.仿真结果表明:非对称结构的2*2混合策略演化博弈模型不存在演化均衡.从监管部门的角度出发,改变博弈支付矩阵在演化博弈过程始终保持不变的情况,在博弈支付矩阵中考虑动态惩罚策略.结果表明,本策略的演化博弈模型存在演化稳定均衡,即Nash均衡.系统动力学为演化博弈理论的验证和应用提供一个政策仿真实验平台.     

Scaling of program functionality

The distribution of fitness values (landscapes) of programs tends to a limit as the programs get bigger. We use Markov chain convergence theorems to give general upper bounds on the length of programs needed for convergence. How big programs need to be to approach the limit depends on the type of the computer they run on. We give bounds (exponential in N, N log N and smaller) for five computer models: any, average or amorphous or random, cyclic, bit flip and four functions (AND, NAND, OR and NOR). Programs can be treated as lookup tables which map between their inputs and their outputs. Using this we prove similar convergence results for the distribution of functions implemented by linear computer programs. We show most functions are constants and the remainder are mostly parsimonious. The effect of ad-hoc rules on genetic programming (GP) are described and new heuristics are proposed. We give bounds on how long programs need to be before the distribution of their functionality is close to its limiting distribution, both in general and for average computers. The computational importance of destroying information is discussed with respect to reversible and quantum computers. Mutation randomizes a genetic algorithm population in generations. Results for average computers and a model like genetic programming are confirmed experimentally.     

Incorporating characteristics of human creativity into an evolutionary art algorithm

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.

Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation

Genetic Algorithms (GAs) and other Evolutionary Algorithms (EAs), as powerful and broadly applicable stochastic search and optimization techniques have been successfully applied in the area of management science, operations research and industrial engineering. In the past few years, researchers gave lots of great idea for improvement of evolutionary algorithms, which include population initialization, individual selection, evolution, parameter setting, hybrid approach with conventional heuristics etc. However, though lots of different versions of evolutionary computations have been created, all of them have turned most of its attention to the development of search abilities of approaches. In this paper, for improving the search ability, we focus on how to take a balance between exploration and exploitation of the search space. It is also very difficult to solve problem, because the balance between exploration and exploitation is depending on the characteristic of different problems. The balance also should be changed dynamically depend on the status of evolution process. Purpose of this paper is the design of an effective approach which it can correspond to most optimization problems. In this paper, we propose an auto-tuning strategy by using fuzzy logic control. The main idea is adaptively regulation for taking the balance among the stochastic search and local search probabilities based on the change of the average fitness of parents and offspring which is occurred at each generation. In addition, numerical analyses of different type optimization problems show that the proposed approach has higher search capability that improve quality of solution and enhanced rate of convergence.