新型模拟进化计算

提出了一种新的模拟进化计算,它模仿新宇宙进化理论中膨胀、收缩两种模式交互作用,推动宇宙进步的过程。膨胀操作提高了种群的多样性和进化算法克服局部收敛的能力,收缩操作吸取了不同群体的优良特性,改善了算法的计算效率,实验结果表明该范式是有效的。     

Barcode design by evolutionary computation

This paper proposes a method for generating 2-dimensional barcode incorporated some illustrations inside of the code without detracting machine-readability and stored information. We formulate the task that finding appropriate positions, scales, and angles of illustrations, photographs, logos or other image items put in QR code as an optimization problem. By using evolutionary computation algorithm, the proposed system can find positions in which given image items can be merged without damaging machine-readability. QR code is trademarked by Denso Wave, inc. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adversarial search by evolutionary computation

In this paper, we consider the problem of finding good next moves in two-player games. Traditional search algorithms, such as minimax and alpha-beta pruning, suffer great temporal and spatial expansion when exploring deeply into search trees to find better next moves. The evolution of genetic algorithms with the ability to find global or near global optima in limited time seems promising, but they are inept at finding compound optima, such as the minimax in a game-search tree. We thus propose a new genetic algorithm-based approach that can find a good next move by reserving the board evaluation values of new offspring in a partial game-search tree. Experiments show that solution accuracy and search speed are greatly improved by our algorithm.     

网格计算及其在进化计算中的应用

提出了一种将网格技术应用于进化算法的计算模式。这种计算模式使用规则与规则集共同演变的模式,实现了数据挖掘技术中的分类。它利用网格计算技术的优势,提高了以往复杂的数据挖掘技术的能力和效率。     

Redundant representations in evolutionary computation

This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2(kr)/r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.     

交互式进化计算研究进展

针对交互式进化计算的研究进展进行综述.首先对其求解模式及特点进行描述;然后对其理论、应用与改进研究进行系统分析,并深入讨论了交互式进化计算中的不确定性;最后指出了今后需要进一步研究的问题.     

Image segmentation using evolutionary computation

Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented     

Theoretical foundations of dynamic program slicing

This paper presents a theory of dynamic slicing, which reveals that the relationship between static and dynamic slicing is more subtle than previously thought. The definitions of dynamic slicing are formulated in terms of the projection theory of slicing. This shows that existing forms of dynamic slicing contain three orthogonal dimensions in their slicing criteria and allows for a lattice-theoretic study of the subsumption relationship between these dimensions and their relationship to static slicing formulations.     

Multiobjective evolutionary computation for supersonic wing-shapeoptimization

This paper discusses the design optimization of a wing for supersonic transport (SST) using a multiple-objective genetic algorithm (MOGA). Three objective functions are used to minimize the drag for supersonic cruise, the drag for transonic cruise, and the bending moment at the wing root for supersonic cruise. The wing shape is defined by 66 design variables. A Euler flow code is used to evaluate supersonic performance, and a potential flow code is used to evaluate transonic performance. To reduce the total computational time, flow calculations are parallelized on an NEC SX-4 computer using 32 processing elements. The detailed analysis of the resulting Pareto front suggests a renewed interest in the arrow wing planform for the supersonic wing     

Biclustering of expression data with evolutionary computation

Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy. The goal is to find biclusters of maximum size with mean squared residue lower than a given /spl delta/. In addition, we pay special attention to the fact of looking for high-quality biclusters with large variation, i.e., with a relatively high row variance, and with a low level of overlapping among biclusters. The quality of biclusters found by our evolutionary approach is discussed and the results are compared to those reported by Cheng and Church, and Yang et al. In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.     

Embodiment of evolutionary computation in general agents

Holland's Adaptation in Natural and Artificial Systems largely dealt with how systems, comprised of many self-interested entities, can and should adapt as a whole. This seminal book led to the last 25 years of work in genetic algorithms (GAs) and related forms of evolutionary computation (EC). In recent years, the expansion of the Internet, other telecommunications technologies, and other large scale networks have led to a world where large numbers of semi-autonomous software entities (i.e., agents) will be interacting in an open, universal system. This development cast the importance of Holland's legacy in a new light. This paper argues that Holland's fundamental arguments, and the years of developments that have followed, have a direct impact on systems of general network agents, regardless of whether they explicitly exploit EC. However, it also argues that the techniques and theories of EC cannot be directly transferred to the world of general agents (rather than EC-specific) without examination of effects that are embodied in general software agents. This paper introduces a framework for EC interchanges between general-purpose software agents. Preliminary results are shown that illustrate the EC effects of asynchronous actions of agents within this framework. Building on this framework, coevolutionary agents that interact in a simulated producer/consumer economy are introduced. Using these preliminary results as illustrations, areas for future investigation of embodied EC software agents are discussed.     

Solving equations by hybrid evolutionary computation techniques

Evolutionary computation techniques have mostly been used to solve various optimization and learning problems. This paper describes a novel application of evolutionary computation techniques to equation solving. Several combinations of evolutionary computation techniques and classical numerical methods are proposed to solve linear and partial differential equations. The hybrid algorithms have been compared with the well-known classical numerical methods. The experimental results show that the proposed hybrid algorithms outperform the classical numerical methods significantly in terms of effectiveness and efficiency     

一种新的基于进化计算的聚类算法

聚类是数据挖掘领域的重要研究内容之一。针对遗传聚类算法较好的稳定性与粒子群优化算法较强的局部搜索能力,在交叉、变异算子后叠加粒子群优化算子的方法实现了二者的结合,提出了GAPSO聚类算法,既保持了遗传算法的稳定性与泛化性的优势,又发挥了PSO算法收敛效率高的特点。通过对10组二维空间上的聚类样本进行实验研究显示,GAPSO聚类算法在收敛效率上显著优于GA聚类算法,在稳定性上优于PSO聚类算法。     

Evolving Nash-optimal poker strategies using evolutionary computation

This paper focuses on the development of a competitive computer player for the one versus one Texas Hold’em poker using evolutionary algorithms (EA). A Texas Hold’em game engine is first constructed where an efficient odds calculator is programmed to allow for the abstraction of a player’s cards, which yield important but complex information. Effort is directed to realize an optimal player that will play close to the Nash equilibrium (NE) by proposing a new fitness criterion. Preliminary studies on a simplified version of poker highlighted the intransitivity nature of poker. The evolved player displays strategies that are logical but reveals insights that are hard to comprehend e.g., bluffing. The player is then benchmarked against Poki and PSOpti, which is the best heads-up Texas Hold’em artificial intelligence to date and plays closest to the optimal Nash equilibrium. Despite the much constrained chromosomal strategy representation, simulated results verified that evolutionary algorithms are effective in creating strategies that are comparable to Poki and PSOpti in the absence of expert knowledge.     

Asynchronous and implicitly parallel evolutionary computation models

This paper presents the design and the application of asynchronous models of parallel evolutionary algorithms. An overview of the existing parallel evolutionary algorithm (PEA) models and available implementations is given. We present new PEA models in the form of asynchronous algorithms and implicit parallelization, as well as experimental data on their efficiency. The paper also discusses the definition of speedup in PEAs and proposes an appropriate speedup measurement procedure. The described parallel EA algorithms are tested on problems with varying degrees of computational complexity. The results show good efficiency of asynchronous and implicit models compared to existing parallel algorithms.     

分层交互式进化计算及其应用

针对目前交互式进化计算存在的局部搜索能力不强、效宰低下等问题，将分层的思想引入交互式进化计算，提出了分层交互式进化计算．给出了算法实施的关键问题，分析了算法的效宰．将其应用于服装设计，通过算例验证了算法的有效性．     

Parallelizing evolutionary computation: A mobile agent-based approach

Parallel computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and they have been implemented on parallel computers to speed up the computation. Instead of using expensive parallel computing facilities, we propose to implement parallel evolutionary computation models on easily available networked PCs, and present a multi-agent framework to support parallelism. With the unique characteristics of agent autonomy and mobility, mobile agents can carry the EC-code and migrate from machine to machine to complete the computation dynamically. To evaluate the proposed approach we have developed a prototype system on a middleware platform JADE to solve a time-consuming task. Different kinds of experiments have been conducted to assess the developed system and the preliminary results show the promise and efficiency of our mobile agent-based approach.     

一种计算话务量的进化神经网络方法

话务量是度量用户使用电话设备频繁程度的一个重要参量,由于目前话务分布呈现出显著的立体性、多业务性和非泊松流等特点,不能直接应用欧兰Ｂ公式进行计算。为此,从计算智能出发提出一种基于PSO算法的进化神经计算方法,主要包括话务量及其相关参量的获取、神经网络结构的优化、基于PSO算法的网络训练,以及话务量计算等步骤。通过对河北省某市小灵通业务的详细研究,利用近半年来的话务量与无线阻塞率、来话接通率和掉话率等参量构成的样本信息进行建模,所计算的话务量精度高,表明其方法切实可行且效果显著。