基于演化周期的惩罚策略

遗传归纳逻辑程序设计(GILP)的个体编码生长现象严重影响了算法的性能和规则的可读性。但从初始种群开始添加长度惩罚项来解决个体编码生长问题时,种群会出现退化现象。而采取在演化的初期不添加惩罚项,在种群的性状有了明显改善后再添加惩罚的策略,既可避免种群退化,又可有效解决个体编码生长问题。     

基于位串编码的遗传归纳逻辑程序设计

归纳逻辑程序设计是基于一阶逻辑的数据挖掘新方法。一阶规则挖掘是目标谓词和背景知识谓词对应的各种原子的复杂组合优化问题。该文根据Occam’s razor原理提出原子的位串编码,设计相应的遗传箅子,基于sequential covering策略提出采用遗传算法作为搜索策略的遗传归纳逻辑程序设计算法GILP。在连通图问题和gcd问题上验证算法的可行性。     

选择策略对遗传归纳逻辑程序设计收敛性能的影响

采用遗传算法（GA）作为归纳逻辑程序设计（ILP）的搜索策略，可以提高ILP方法的鲁棒性和适应性，文章简要叙述了对作者提出的遗传归纳逻辑程序设计（GILP）算法作的改进，测试了选择策略对GILP算法收敛性能的影响，采用不同的选择策略不会影响算法的最终收敛结果，但会产生不同的选择压力，导致算法具有不同的收敛速率。     

归纳逻辑程序设计综述

归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域,是机器学习的前沿研究课题。该文首先从归纳逻辑程序设计的问题背景、类型划分和搜索程序子句三个方面介绍了归纳逻辑程序设计系统的概貌;然后结合实验室的相关研究工作,回顾了归纳逻辑程序设计研究的发展;之后介绍了归纳逻辑程序设计领域中需要深入研究的若干问题,并提出了新的解决思路;最后是总结,以引起读者对归纳逻辑程序设计领域研究的进一步关注。     

生成树编码遗传算法种群退化分析及抑制方法

种群退化现象导致了遗传算法对解空间区域进行重复搜索,从而降低了算法的搜索效率和延缓了算法的收敛,这源于重组算子、采样误差和变异算子的反作用力。通过对生成树编码遗传算法的研究,分析了重组算子的种群退化现象。证明了在解决固定费用运输问题时,重组算子发生种群退化现象的一个充分条件及其概率。针对种群退化现象提出了基于概率选择模型抑制算法（Probabilistic Selection Model Crossover,PSDC）,对其有效性进行了分析证明。与小生境技术相比,它具有可以通过控制选择概率来抑制种群退化和不需要额外的时间开销两大优势,这为遗传算法的设计和应用提供了理论研究依据。     

约束归纳逻辑程序设计系统

提出了一种新的约束归纳逻辑程序设计方法，并初步实现了一个自顶向下的约束归纳逻辑程序原型系统。该系统能够导出不受变量个数限制的多种形式的线性约束，得出覆盖正例而排斥负例的含约束的Hom子句程序。     

基于遗传算法的一阶规则获取

基于一阶谓词逻辑的一阶规则挖掘方法可处理多表挖掘且具有强的知识表达能力,成为数据挖掘(DM)技术中一种渐受重视的新方法。为了解决现有方法规则获取的性能瓶颈问题,该文提出了一种新的基于遗传算法的一阶规则挖掘算法(GILP)。实验结果表明,GILP算法能有效地挖掘一阶规则。     

采用改进的混合遗传算法求解高校排课问题

为了解决一个存在大量合班现象的高校排课问题,建立了相应的数学模型并采用改进的混合遗传算法进行了求解。在产生初始种群的过程中进行了乱序处理,以提高初始种群中个体的多样性,避免早熟收敛现象的发生;为了防止种群的退化,引入了保留最优个体策略和竞争机制;根据问题的特点设计了与之相适应的遗传算子;为了提高种群进化的效率,交叉概率和变异概率都使用了自适应参数;为了提高算法的局部搜索能力,在交叉操作阶段采用了模拟退火算法。通过Matlab与Access混合编程,实现了对大规模数据的高效处理。实例结果表明,该算法能够有效地解决存在合班现象的高校排课问题。     

基于外点法的混合遗传算法求解约束优化问题

提出了一种求解约束优化问题的混合遗传算法。它不是传统的在适应值函数中加一个惩罚项，而是在初始种群、交叉运算和变异运算过程中，把违反约束条件的个体用外点法处理设计出新的实数编码遗传算法。数值实验证明，新算法性能优于现有其他进化算法，是通用性强、高效稳健的方法。该方法兼顾了遗传算法和外点法的优点，既有较快的收敛速度，又能以非常大的概率求得约束优化问题全局最优解。     

一种维持种群多样性的遗传算法变异算子的研究

本文针对二进制编码遗传算法中，由于传统变异算子随机地选取基因位置而对搜索全局最优的不利影响，分析了变异位置对种群多样性的影响．提出了一种新的维持种群多样性的变异算子，其变异概率和变异位置由种群基因位的多样度和个体适应度值自适应决定．经变异后优秀的个体得以保存，且在种群中每一基因坐上两种基因的比例控制在期望的范围内．本文最后用实验验证了该算于维持种群多样性的有效性．     

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.     

遗传程序设计(GP)的适应性函数设计

适应性函数是遗传算法和遗传程序设计得以实现的关键因素,是评价个体好坏的定量表述,决定着演化过程中群体选择复制及群体整体性的质量。该文叙述了适应性函数的设计方法,解释各设计方法的原因,并提出根据树型遗传算法的个体特点通过控制适应性函数大小对个体进行奖励惩罚。     

An Analysis of the Causes of Code Growth in Genetic Programming

This research examines the cause of code growth (bloat) in genetic programming (GP). Currently there are three hypothesized causes of code growth in GP: protection, drift, and removal bias. We show that single node mutations increase code growth in evolving programs. This is strong evidence that the protective hypothesis is correct. We also show a negative correlation between the size of the branch removed during crossover and the resulting change in fitness, but a much weaker correlation for added branches. These results support the removal bias hypothesis, but seem to refute the drift hypothesis. Our results also suggest that there are serious disadvantages to the tree structured programs commonly evolved with GP, because the nodes near the root are effectively fixed in the very early generations.     

改进罚函数法与蝙蝠算法在约束优化中的应用

设计了一种基于自适应罚函数法和改进蝙蝠算法的约束优化问题求解方法。提出了一种自适应罚函数法,该处理方法综合考虑了约束违反的情况和进化过程的特点,如果某个约束违反的次数越多,则证明该约束越强,赋予惩罚系数越大;种群中的不可行解的数量越多,为保持种群的多样性,则约束应该取较小的值,即惩罚系数取较小的值。提出了一种改进的蝙蝠算法,利用混沌的遍历性特点产生初始种群,增强了初始种群的多样性和种群的质量;在考虑了脉冲响度的蝙蝠算法局部搜索中,融入了交叉操作;为防止算法在后期陷入局部最优解,引进了变异操作,保证了群体的多样性。将自适应罚函数法与改进的蝙蝠算法融合起来求解约束优化问题,4个复杂的标准测试函数和2个工程实际问题证明了该约束优化求解方法的可行性和有效性。     

一种基于多样性保持的遗传算法及其仿真

针对遗传算法存在的早熟收敛,为提高优化效果,提出了一种基于多样性保持的自适应遗传算法。首先引入了一种基于适应度方差的个体多样性度量指标,并利用指标对交叉概率进行自适应调整,以维持种群中的个体多样性。其次,通过计算各基因座上等位基因的基因多样性,自适应调整相应基因座上等位基因的变异概率,以增强基因多样性、抑制种群中的有效基因缺失,并进行仿真。仿真结果表明,算法具有不易陷入局部最优,解的精度较高,达到优化效果的要求。     

基于惩罚函数的演化测试优化方法研究

演化测试是一种有效的测试用例自动生成技术。它将测试用例的生成的过程转化为一个利用遗传算法进行数值优化的问题,可以自动生成高质量的测试用例。目前,演化测试技术已经在许多测试领域被广泛地研究。研究发现,演化测试过程中无效解和局部最优解的存在扰乱了演化正常进行,降低了演化测试的性能。针对演化测试中存在的问题,提出基于惩罚函数的适应值函数F（x）,对无效解和局部最优解进行惩罚,有助于演化过程中根据适应值选择更合适的个体进行遗传操作,提高演化测试的性能。实验结果表明,F（x）有效降低了无效解和局部最优解的适应值,提高了演化测试的性能。     

A collaborative filtering-based approach to personalized document clustering

Document clustering is an intentional act that reflects individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document clustering must consider individual preferences and needs to support personalization in document categorization. Most existing document-clustering techniques, generally anchoring in pure content-based analysis, generate a single set of clusters for all individuals without tailoring to individuals' preferences and thus are unable to support personalization. The partial-clustering-based personalized document-clustering approach, incorporating a target individual's partial clustering into the document-clustering process, has been proposed to facilitate personalized document clustering. However, given a collection of documents to be clustered, the individual might have categorized only a small subset of the collection into his or her personal folders. In this case, the small partial clustering would degrade the effectiveness of the existing personalized document-clustering approach for this particular individual. In response, we extend this approach and propose the collaborative-filtering-based personalized document-clustering (CFC) technique that expands the size of an individual's partial clustering by considering those of other users with similar categorization preferences. Our empirical evaluation results suggest that when given a small-sized partial clustering established by an individual, the proposed CFC technique generally achieves better clustering effectiveness for the individual than does the partial-clustering-based personalized document-clustering technique.     

Binary String Fitness Characterization and Comparative Partner Selection in Genetic Programming

The premise behind all evolutionary methods is “survival of the fittest,” and consequently, individuals require a quantitative fitness measure. This paper proposes a novel strategy for evaluating individual's relative strengths and weaknesses, as well as representing these in the form of a Binary String Fitness Characterization (BSFC); in addition, as customary, an overall fitness value is assigned to each individual. Utilizing the BSFC, we demonstrate both novel population evaluation measures and a pairwise mating strategy, Comparative Partner Selection (CPS), with the aim of evolving a population that promotes effective solutions by reducing population-wide weaknesses. This strategy is tested with six standard genetic programming benchmarking problems.