A novel evolutionary algorithm inspired by the states of matter for template matching

Template matching (TM) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. The TM approach seeks the best possible resemblance between a sub-image, known as template, and its coincident region within a source image. TM has two critical aspects: similarity measurement and search strategy. The simplest available TM method finds the best possible coincidence between the images through an exhaustive computation of the Normalized Cross-Correlation (NCC) value (similarity measurement) for all elements in the source image (search strategy). Unfortunately, the use of such approach is strongly restricted since the NCC evaluation is a computationally expensive operation. Recently, several TM algorithms that are based on evolutionary approaches, have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the states of matter phenomenon is proposed to reduce the number of search locations in the TM process. In the proposed approach, individuals emulate molecules that experiment state transitions which represent different exploration–exploitation levels. In the algorithm, the computation of search locations is drastically reduced by incorporating a fitness calculation strategy which indicates when it is feasible to calculate or to only estimate the NCC value for new search locations. Conducted simulations show that the proposed method achieves the best balance in comparison to other TM algorithms considering the estimation accuracy and the computational cost.     

Hoeffding bound based evolutionary algorithm for symbolic regression

In symbolic regression area, it is difficult for evolutionary algorithms to construct a regression model when the number of sample points is very large. Much time will be spent in calculating the fitness of the individuals and in selecting the best individuals within the population. Hoeffding bound is a probability bound for sums of independent random variables. As a statistical result, it can be used to exactly decide how many samples are necessary for choosing i individuals from a population in evolutionary algorithms without calculating the fitness completely. This paper presents a Hoeffding bound based evolutionary algorithm (HEA) for regression or approximation problems when the number of the given learning samples is very large. In HEA, the original fitness function is used in every k generations to update the approximate fitness obtained by Hoeffding bound. The parameter 1?δ is the probability of correctly selecting i best individuals from population P, which can be tuned to avoid an unstable evolution process caused by a large discrepancy between the approximate model and the original fitness function. The major advantage of the proposed HEA algorithm is that it can guarantee that the solution discovered has performance matching what would be discovered with a traditional genetic programming (GP) selection operator with a determinate probability and the running time can be reduced largely. We examine the performance of the proposed algorithm with several regression problems and the results indicate that with the similar accuracy, the HEA algorithm can find the solution more efficiently than tradition EA. It is very useful for regression problems with large number of training samples.     

模型降阶和参数估计的一种快速遗传算法

针对传统遗传算法求解模型降阶和参数估计时,适配值评价既费时又效率较低的缺点,对实数编码GA引入相似度和可信度的概念,采用插值方法进行适配值评价,大大减少了评价环节的计算量,提高了整个算法的效率和实时性．通过对典型模型降阶和参数估计问题的仿真,验证了所提出方法的可行性和有效性．     

Template matching using an improved electromagnetism-like algorithm

Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the best-possible coincidence between the images through an exhaustive computation of the normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetism-like algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the re-evaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the best-possible resemblance is determined. The conducted simulations show that the proposed method achieves the best balance over other TM algorithms in terms of estimation accuracy and computational cost.     

An efficient differential evolution using speeded-up k-nearest neighbor estimator

In evolutionary algorithm, one of the main issues is how to reduce the number of fitness evaluations required to obtain optimal solutions. Generally a large number of evaluations are needed to find optimal solutions, which leads to an increase of computational time. Expensive cost may have to be paid for fitness evaluation as well. Differential evolution (DE), which is widely used in many applications due to its simplicity and good performance, also cannot escape from this problem. In order to solve this problem a fitness approximation model has been proposed so far, replacing real fitness function for evaluation. In fitness approximation, an ability to estimate accurate value with compact structure is needed for good performance. Therefore in this paper we propose an efficient differential evolution using fitness estimator. We choose k-nearest neighbor (kNN) as fitness estimator because it does not need any training period or complex computation. However too many training samples in the estimator may cause computational complexity to be exponentially high. Accordingly, two schemes with regard to accuracy and efficiency are proposed to improve the estimator. Our proposed algorithm is tested with various benchmark functions and shown to find good optimal solutions with less fitness evaluation and more compact size, compared with DE and DE-kNN.     

协同演化算法研究进展

协同演化算法(coevolutionary algorithms,CEA)是当前国际上计算智能研究的一个热点,它运用生物协同演化的思想,是针对演化算法的不足而兴起的,通过构造两个或多个种群,建立它们之间的竞争或合作关系,多个种群通过相互作用来提高各自性能,适应复杂系统的动态演化环境,以达到种群优化的目的.介绍了协同演化算法的研究状况以及目前的研究进展,概述了它的基本算法、主要特点、理论与技术,同时介绍了一些主要的应用领域,指出了协同演化算法的研究方向.     

The irradiance volume

A major goal in computer graphics is realistic image synthesis. To this end, illumination methods have evolved from simple local shading models to physically based global illumination algorithms. Local illumination methods consider only the light energy transfer between an emitter and a surface (direct lighting), while global methods account for light energy interactions between all surfaces in an environment, considering both direct and indirect lighting. Even though the realistic effects that global illumination algorithms provide are frequently desirable, the computational expense of these methods is too great for many applications. Dynamic environments and scenes containing a very large number of surfaces often pose problems for global illumination methods. This article presents a different approach to calculating the global illumination of objects. Instead of striving for accuracy at the expense of performance, we rephrase the goal: to achieve a reasonable approximation with high performance. This places global illumination effects within reach of many applications in which visual appearance is more important than absolute numerical accuracy     

Who but not where: The effect of social play on immersion in digital games

The majority of digital games available today offer a variety of multi-player settings including co-located and mediated play between opponents. Immersion, the sense of being “in the game,” is one of the key components of the gaming experience but existing literature suggests that social play provides more fun but less immersion. There is however little empirical support for this. This paper therefore addresses the question: how does playing digital games in a social situation alter the sense of immersion felt by the individuals playing? This paper presents three experiments that test the relationship between social setting and immersion. The three experiments aim to manipulate the social setting in which players play, be it against a computer, against a person online or against a co-located person. Overall the three experiments show that players are more immersed when playing against another person rather than playing against a computer but there is no significant difference in immersion whether the other person is online or in the same room. This refutes previous claims about social play reducing immersion and indeed that social play enhances the sense of being in the game where interaction is through the game.     

Approaching zero [attack trends]

The convergence of criminals with technically savvy crackers continues. The result of that trend is that the risk of zero-day vulnerabilities increases as financially motivated individuals look to break into computer systems and take advantage of others computer resources to spam, install adware, create bot networks to threaten and blackmail Web sites, or steal sensitive financial information. The lure of money is changing the computer security playing field. We must reexamine our assumptions in the face of financially motivated attackers.     

Context and Driving Forces in the Development of the Early Computer Game Nimbi

The Danish computer company Regnecentralen developed a computer version of Piet Hein's game Nimbi, a variant of the ancient game Nim, in 1962 and 1963. Piet Hein envisioned computers playing against humans, while Regnecentralen hoped Nimbi would illustrate the potential of computers to the public. Although technologically successful, Nimbi never fulfilled its promise due to hardware constraints and a lack of organizational follow-up.     

Evolutionary computation and games

Games provide competitive, dynamic environments that make ideal test beds for computational intelligence theories, architectures, and algorithms. Natural evolution can be considered to be a game in which the rewards for an organism that plays a good game of life are the propagation of its genetic material to its successors and its continued survival. In natural evolution, the fitness of an individual is defined with respect to its competitors and collaborators, as well as to the environment. Within the evolutionary computation (EC) literature, this is known as co-evolution and within this paradigm, expert game-playing strategies have been evolved without the need for human expertise.     

DEMORS: A hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems

The aim of this paper is to show how the hybridization of a multi-objective evolutionary algorithm (MOEA) and a local search method based on the use of rough set theory is a viable alternative to obtain a robust algorithm able to solve difficult constrained multi-objective optimization problems at a moderate computational cost. This paper extends a previously published MOEA [Hernández-Díaz AG, Santana-Quintero LV, Coello Coello C, Caballero R, Molina J. A new proposal for multi-objective optimization using differential evolution and rough set theory. In: 2006 genetic and evolutionary computation conference (GECCO’2006). Seattle, Washington, USA: ACM Press; July 2006], which was limited to unconstrained multi-objective optimization problems. Here, the main idea is to use this sort of hybrid approach to approximate the Pareto front of a constrained multi-objective optimization problem while performing a relatively low number of fitness function evaluations. Since in real-world problems the cost of evaluating the objective functions is the most significant, our underlying assumption is that, by aiming to minimize the number of such evaluations, our MOEA can be considered efficient. As in its previous version, our hybrid approach operates in two stages: in the first one, a multi-objective version of differential evolution is used to generate an initial approximation of the Pareto front. Then, in the second stage, rough set theory is used to improve the spread and quality of this initial approximation. To assess the performance of our proposed approach, we adopt, on the one hand, a set of standard bi-objective constrained test problems and, on the other hand, a large real-world problem with eight objective functions and 160 decision variables. The first set of problems are solved performing 10,000 fitness function evaluations, which is a competitive value compared to the number of evaluations previously reported in the specialized literature for such problems. The real-world problem is solved performing 250,000 fitness function evaluations, mainly because of its high dimensionality. Our results are compared with respect to those generated by NSGA-II, which is a MOEA representative of the state-of-the-art in the area.     

基于共轭增强策略的差分进化算法

为了平衡差分进化算法的全局探测能力和局部搜索能力,提出基于共轭增强策略的差分进化算法.首先,根据个体适应度信息设计基于轮盘赌的个体选择策略,选取适应值较差的个体组建子种群;然后,基于个体的时间和空间知识设计共轭增强方向,在不丧失全局探测能力的前提下实现子种群的局部增强,以提高算法的局部搜索能力;最后,18个标准测试函数的实验结果表明,所提算法在计算代价、可靠性及收敛速度方面均优于所介绍的主流改进差分进化算法和非差分进化算法.     

教育游戏开发原则与技术

当今电脑游戏发展迅速,青少年对其的迷恋已经成为社会关注的问题。研究电脑游戏的教育价值,将其与教育相结合是目前研究的重点。游戏实现技术多种多样,掌握难度较高,该文主要介绍了一种操作简单、功能强大的游戏设计软件——RPGMaker。     

Block-matching algorithm based on harmony search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.     

Parametric image alignment using enhanced correlation coefficient maximization

In this work we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative schemes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As it is customary in iterative optimization, in each iteration the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case we propose an efficient approximation that leads to a closed form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the Forward Additive Lucas-Kanade and the Simultaneous Inverse Compositional algorithm through simulations. Under noisy conditions and photometric distortions our forward version achieves more accurate alignments and exhibits faster convergence whereas our inverse version has similar performance as the Simultaneous Inverse Compositional algorithm but at a lower computational complexity.     

Designing for collaboration: a study in intergenerational social game design

This paper presents a study of a computer game designed for the elderly, allowing them to train their memory while playing the game. The game supports both a single-player and a multiplayer mode, in which the elderly can play with their friends or family using an embedded video chat application. The main question that is addressed in this paper is how the elderly gamers’ experience is influenced by the possibility to communicate directly with the other players. The study presents a comparison of the game experience and appreciation of older users and their (grand)children playing the game together, with or without the video chat application. Most importantly, the study shows that the added value of video chat is not limited to social contact, but that it also provides opportunities for the younger generation to assist the elderly during the game. In conclusion, the paper points out some intergenerational game design implications, and some future research suggestions.     

基于WinCE应用程序的围棋游戏开发

机器博弈,也称计算机博弈,即让计算机下棋。围棋是一种策略性二人棋类游戏,使用格状棋盘及黑白二色棋子进行对弈。文中计算机围棋游戏引擎的开发采用马尔科夫决策模型,使用人工智能的知识,含有大量计算,整个计算紧密依赖于系统资源,计算量越大,引擎的选点越精确,棋力越高。针对嵌入式系统软硬件的特定性,其资源和计算能力的局限性,本文主要完成了两个工作：一是将实验室适用于PC的游戏引擎移植到WinCE,开发适合嵌入式系统的围棋游戏引擎,实现大规模计算的移植,使游戏引擎在嵌入式有限的资源上,通过精简的计算量,达到不错的效果;二是实现WinCE上围棋游戏前台界面的开发。     

Model-based reinforcement learning for partially observable games with sampling-based state estimation

Games constitute a challenging domain of reinforcement learning (RL) for acquiring strategies because many of them include multiple players and many unobservable variables in a large state space. The difficulty of solving such realistic multiagent problems with partial observability arises mainly from the fact that the computational cost for the estimation and prediction in the whole state space, including unobservable variables, is too heavy. To overcome this intractability and enable an agent to learn in an unknown environment, an effective approximation method is required with explicit learning of the environmental model. We present a model-based RL scheme for large-scale multiagent problems with partial observability and apply it to a card game, hearts. This game is a well-defined example of an imperfect information game and can be approximately formulated as a partially observable Markov decision process (POMDP) for a single learning agent. To reduce the computational cost, we use a sampling technique in which the heavy integration required for the estimation and prediction can be approximated by a plausible number of samples. Computer simulation results show that our method is effective in solving such a difficult, partially observable multiagent problem.     

Aggression,competition and computer games: computer and human opponents

Violence and aggression in computer games has been a concern of social commentators and an interest of media researchers for more than 10 years. Violent content has been at the top of the agenda even though aggression and hostility have been identified as a part of competitive gaming situations. The role of the opponent in this process has been largely overlooked. We examined the difference in frustration and aggression in game play after users encountered the computer as opponent and a proximate person as opponent using the same CD-ROM version of Monopoly. We found that users experienced higher levels of aggressive feelings after playing the computer than after playing a stranger face-to-face. It appears that aggression related to computer gaming may be reduced through the humanization of computer opponents.