基于进化计算的中医脉象分类识别研究

为了实现中医脉象的客观、准确分类,提出了一种基于进化计算的脉象识别方法.提出一种改进的GEP算法(IGEP),并对IGEP算法进行了复杂度分析.基于IGEP,设计出了对脉象信号进行自动建模识别的系统.最后用大量临床脉象样本对算法建模的函数进行了检验,实验结果表明该方法能够实现对中医常见脉象的准确、快速分类.     

Designing a classifier by a layered multi-population genetic programming approach

This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time.     

进化计算在神经网络学习中的应用

提出两种用于前向神经网络的进化学习算法,一种基于遗传算法,另一种基于进化规则,通过对ＸＯＲ问题和ＩＲＩＳ模式分类问题的学习,证明它们远高于传统ＢＰ算法的性能。     

混合性能指标优化问题的进化优化方法及应用

针对混合性能指标优化问题的普遍性及其处理过程中的特点,提出一种混合性能指标优化问题的进化优化方法,首先给出混合性能指标优化问题的定义;然后．确定不同类型和标度的性能指标的转换策略、混合性能指标的个体适应度的赋值方法、以及混合性能指标优化问题的进化优化流程;最后．通过服装设计这一典型的混合性能指标优化问题的仿真验证了算法的有效性．     

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.     

Backward-chaining evolutionary algorithms

Starting from some simple observations on a popular selection method in Evolutionary Algorithms (EAs)—tournament selection—we highlight a previously-unknown source of inefficiency. This leads us to rethink the order in which operations are performed within EAs, and to suggest an algorithm—the EA with efficient macro-selection—that avoids the inefficiencies associated with tournament selection. This algorithm has the same expected behaviour as the standard EA but yields considerable savings in terms of fitness evaluations. Since fitness evaluation typically dominates the resources needed to solve any non-trivial problem, these savings translate into a reduction in computer time. Noting the connection between the algorithm and rule-based systems, we then further modify the order of operations in the EA, effectively turning the evolutionary search into an inference process operating in backward-chaining mode. The resulting backward-chaining EA creates and evaluates individuals recursively, backward from the last generation to the first, using depth-first search and backtracking. It is even more powerful than the EA with efficient macro-selection in that it shares all its benefits, but it also provably finds fitter solutions sooner, i.e., it is a faster algorithm. These algorithms can be applied to any form of population based search, any representation, fitness function, crossover and mutation, provided they use tournament selection. We analyse their behaviour and benefits both theoretically, using Markov chain theory and space/time complexity analysis, and empirically, by performing a variety of experiments with standard and back-ward chaining versions of genetic algorithms and genetic programming.     

Evolving choice structures for genetic programming

It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some “if-structures” functions according to their problems by combining “if-else” statement, conditional criterions and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination criterion in the GP process named “Combination Termination Criterion” (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.     

Evolving transfer functions for artificial neural networks

The paper describes a methodology for constructing transfer functions for the hidden layer of a back-propagation network, which is based on evolutionary programming. The method allows the construction of almost any mathematical form. It is tested using four benchmark classification problems from the well-known machine intelligence problems repository maintained by the University of California, Irvine. It was found that functions other than the commonly used sigmoidal function could perform well when used as hidden layer transfer functions. Three of the four problems showed improved test results when these evolved functions were used.     

A Hybrid approach for integer programming combining genetic algorithms, linear programming and ordinal optimization

Hybrid methods are promising tools in integer programming, as they combine the best features of different methods in a complementary fashion. This paper presents such a framework, integrating the notions of genetic algorithm, linear programming, and ordinal optimization in an effort to shorten computation times for large and/or difficult integer programming problems. Capitalizing on the central idea of ordinal optimization and on the learning capability of genetic algorithms to quickly generate good feasible solutions, and then using linear programming to solve the problem that results from fixing the integer part of the solution, one may be able to obtain solutions that are close to optimal. Indeed ordinal optimization guarantees the quality of the solutions found. Numerical testing on a real-life complex scheduling problem demonstrates the effectiveness and efficiency of this approach.     

基于PSO信息传递模式的混合算法

粒子群优化算法（Particle Swarm Optimization,PSO）是一种基于群智能（Swarm Intelligence）的随机优化计算技术。PSO和遗传算法这两种算法相比较,PSO收敛快速准确,但编码形式单一,局限于解决实优化问题,而遗传算法编码形式灵活,解决问题广泛,但执行效率低于PS00。将粒子群算法的信息传递模式与遗传算法的编码和遗传操作相结合,提出一种混合算法。并推导了两个算法之间的密切联系。并通过组合优化和函数优化的基准测试集对算法进行测试,试验结果表明,该算法在收敛精度和速度优于传统遗传算法。同时,也观察到该算法取得了与粒子群算法一致的收敛现象。     

Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming

This work describes a way of designing interest point detectors using an evolutionary-computer-assisted design approach. Nowadays, feature extraction is performed through the paradigm of interest point detection due to its simplicity and robustness for practical applications such as: image matching and view-based object recognition. Genetic programming is used as the core functionality of the proposed human-computer framework that significantly augments the scope of interest point design through a computer assisted learning process. Indeed, genetic programming has produced numerous interest point operators, many with unique or unorthodox designs. The analysis of those best detectors gives us an advantage to achieve a new level of creative design that improves the perspective for human-machine innovation. In particular, we present two novel interest point detectors produced through the analysis of multiple solutions that were obtained through single and multi-objective searches. Experimental results using a well-known testbed are provided to illustrate the performance of the operators and hence the effectiveness of the proposal.     

神经元模式分类器学习的进化计算算法

本文提出了一种用于神经元模式分类器学习的进化计算算法。该算法综合了非确定有限自动机和次群体的动态数据结构,可有效地完成神经网络模式分类器的结构学习,以获得最优的求解结果。该算法的有效性已由计算机仿真实验所证实,可被认为是一种很有发展前途的模式分类系统的机器学习算法。     

Resolution of pattern recognition problems using a hybrid Genetic/Random Neural Network learning algorithm

Gelenbe has proposed a neural network, called a Random Neural Network, which calculates the probability of activation of the neurons in the network. In this paper, we propose to solve the patterns recognition problem using a hybrid Genetic/Random Neural Network learning algorithm. The hybrid algorithm trains the Random Neural Network by integrating a genetic algorithm with the gradient descent rule-based learning algorithm of the Random Neural Network. This hybrid learning algorithm optimises the Random Neural Network on the basis of its topology and its weights distribution. We apply the hybrid Genetic/Random Neural Network learning algorithm to two pattern recognition problems. The first one recognises or categorises alphabetic characters, and the second recognises geometric figures. We show that this model can efficiently work as associative memory. We can recognise pattern arbitrary images with this algorithm, but the processing time increases rapidly.     

一种求解混合整数规划的混合进化算法

提出一种基于正交试验设计的混合进化算法,用于求解混合整数规划问题.进化算法中采用一种混合启发式的变异算子,将正交试验设计作为杂交算子.为了增加种群的多样性,引入一种迁移算子.仿真实验结果表明,与已有的一些算法相比,所提出的求解混合整数规划的混合进化算法能快速收敛到问题的最优解,并且算法的计算量小,解的精度高.     

Genetic optimization of GRNN for pattern recognition without feature extraction

This paper describes an approach for pattern recognition using genetic algorithm and general regression neural network (GRNN). The designed system can be used for both 3D object recognition from 2D poses of the object and handwritten digit recognition applications. The system does not require any preprocessing and feature extraction stage before the recognition. In GRNN, placement of centers has significant effect on the performance of the network. The centers and widths of the hidden layer neuron basis functions are coded in a chromosome and these two critical parameters are determined by the optimization using genetic algorithms. Experimental results show that the optimized GRNN provides higher recognition ability compared with that of unoptimized GRNN.     

A parallel double-level multiobjective evolutionary algorithm for robust optimization

Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.