Solving the balance problem of massively multiplayer online role-playing games using coevolutionary programming

In massively multiplayer online role-playing games (MMORPGs), each race holds some attributes and skills. Each skill contains several abilities such as physical damage and hit rate. All those attributes and abilities are functions of the character's level, which are called Ability-Increasing Functions (AIFs). A well-balanced MMORPG is characterized by having a set of well-balanced AIFs. In this paper, we propose a coevolutionary design method, including integration with the modified probabilistic incremental program evolution (PIPE) and the cooperative coevolutionary algorithm (CCEA), to solve the balance problem of MMORPGs. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs more efficiently, compared with the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA) and the hybrid discrete particle swarm optimization (HDPSO) algorithm. The results also show that the performance of PIPE has been significantly improved through the modification works.     

计及分布式发电和不完全信息可中断负荷选择的电力市场模型

分布式发电（distributed generation,DG）与可中断负荷是智能电网的2种重要资源。针对电力用户停电意愿不尽相同,将配电公司的可中断负荷类型建模为离散分布的随机变量,进而建立具有分布式发电与不完全信息可中断负荷选择的配电公司能量获取模型,综合考虑到发电公司的竞价策略行为,最后建立了完整的不完全信息博弈下的电力市场模型。针对不完全信息博弈的特征,扩展改进了协同进化算法,并用于求解市场贝叶斯纳什均衡。修正的IEEE9节点系统验证了模型与求解方法的有效性。结果表明分布式发电与可中断负荷、发电公司策略行为及不完全信息对市场均衡有着重要的影响。     

An Endosymbiotic Evolutionary Algorithm for Optimization

This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.     

基于混沌的协作型协进化方法

在协作型协进化算法的基础上,提出了基于混沌的协作型协进化方法。该算法加入了进化是否发生停滞的判断,并在发生停滞时进行混沌映射。用经典的函数优化问题进行仿真实验,其结果表明了该算法的有效性。     

容量约束对电力市场均衡的影响

输电容量约束是电力市场的重要特征,同时也深刻影响着电力市场运行。针对协同进化算法能够求取纯策略纳什均衡,多项式方程系统算法能够计算策略式博弈的所有纳什均衡,本文提出一种基于协同进化算法联同多项式方程系统算法的研究方法来分析电力市场纳什均衡,分析输电容量约束对电力市场均衡的作用。文中对一个3机5节点的电力市场进行了实验分析,结果表明该方法能有效求取纯策略市场均衡,同时能够求取一定条件下的混合策略均衡。输电容量约束不仅影响了市场均衡的分布,而且决定了纳什均衡的存在形式。     

A Tournament-Based Competitive Coevolutionary Algorithm

For an efficient competitive coevolutionary algorithm, it is important that competing populations be capable of maintaining a coevolutionary balance and hence, continuing evolutionary arms race to increase the levels of complexity. We propose a competitive coevolutionary algorithm that combines the strategies of neighborhood-based evolution, entry fee exchange tournament competition (EFE-TC) and localized elitism. An emphasis is placed on analyzing the effects of these strategies on the performance of competitive coevolutionary algorithms. We have tested the proposed algorithm with two adversarial problems: sorting network and Nim game problems that have different characteristics. The experimental results show that the interacting effects of the strategies appear to promote a balanced evolution between host and parasite populations, which naturally leads them to keep on evolutionary arms race. Consequently, the proposed algorithm provides good quality solutions with a little computation time.     

Optimal resource allocation for transmission diversity in multi-radio access networks： a coevolutionary genetic algorithm approach

The next generation wireless communication systems aim at supporting enhanced diversified network access and data transmission abilities via the cooperative integration and unified management of various radio access technologies(RATs).The resource allocation is the core component leading the network system and mobile terminals to the service robustness and performance maximization.In this paper,a numeric optimization model for optimizing terminals’transmission power and allocated RAT bandwidth for maximizing system capacity is proposed with the focal consideration of the multi-radio transmission diversity for parallel transmission through multiple links from diferent RATs,and diferent terminal characteristics on RAT supports.Also,we design a centralized and periodic scheduling algorithm including an improved coevolutionary genetic algorithm for efciently solving the optimization problem.Simulation results demonstrate that our propose algorithm can distinctly enhance the system performance and improve the computational efciency.     

一种改进的共同进化遗传算法及其应用

将共同进化遗传算法应用于临床营养决策优化中,虽然取得了一定成果,但算法中仍存在早熟现象、局部搜索能量弱等问题,为此提出一种改进的共同进化模型。算法使用新的子群体划分方法和交叉算子,引入精英保留策略、信息交换原则和小生境技术。通过对糖尿病营养治疗的仿真表明,改进后的算法在保证群体多样性和避免早熟现象的前提下,提高了算法的局部和全局寻优能力,且膳食配方中营养素达标率符合要求,满足专家建议的糖尿病饮食需求。     

组织协同进化分类算法用于雷达目标一维像识别

针对雷达目标一维像识别问题，提出了一种基于组织协同进化分类算法的识别方法．该方法与现有进化分类方法的不同之处在于它的进化操作直接作用于样本而不是规则，采用了一种自下而上的搜索机制，即先使若干样本的集合得到进化，再从进化结果中提取规则．这样有利于避免在进化过程中产生无意义的规则．该方法不需要进行特征提取；对于高维数据，不需要预先进行降维处理；没有复杂的运算，训练和识别的速度都很快．对3种飞机微波暗室实测数据的识别实验表明，该方法性能稳定，优于基于支撑矢量机与子波核函数的方法，识别率均达到了96％以上．实验中还对算法的抗噪能力进行了测试，获得了良好的效果．     

Exploring Coevolutionary Relations by Alterations in Fitness Function: Experiments with Simulated Robots

This paper focuses on various coevolutionary robotic experiments where all parameters except for the fitness function remain the same. Initially an attempt to categorize coevolutionary experiments is made and subsequently three experiments of competitive coevolution (hunt, battle and mating) are presented. The experiment concerning implicit competition of two species (mating) is given special attention as it shows emergence of compromise and collaboration through a competitive environment. The co-evolution progress monitoring is evaluated through fitness graphs, CIAO and Hamming maps and the results are interpreted for each experimental setup. The paper concludes that despite the alteration of fitness functions, several evasion–pursuit elements emerge. Furthermore, conciliatory strategies can emerge in implicit competitional cases.