协同进化

树木对昆虫取食所产生的诱导抗性（InducedResistance）是树木与昆虫长期协同进化的结果。

Induced resistance of tree is the results of coevolution between tree and insect .

非致病微生物在植物-病原菌协同进化中的作用

The Role of Non - pathogenic Microorganisms in Plant-pathogen Coevolution

基于细胞因子网络协同进化的Web服务合成方法

Approach of Web Services Composition Based on Co-evolutionary Cytokine Network

协同进化算法模型在本文进行扩展，应用到Web文本挖掘。

The co-evolutionary model is the one that is expanded to Web text mining .

在多Agent系统研究中的某些领域，一种常用的方法是协同进化多Agent合作。

In some research of multi-agent systems , one natural and popular method is to co-evolve multi-agent behaviors .

基于Pareto协同进化算法的多个FACTS元件协调控制

Coordinative control of multiple FACTS controllers based on Pareto co-evolution algorithm

基于协同进化算法的TS模糊模型设计

Design of TS Fuzzy Model Based on Cooperative Evolutionary Algorithm

Multi-Agent协同进化算法研究

Research on Multi-Agent Co-evolutionary Algorithm

五倍子蚜与寄主植物DNA序列系统发育关系及其协同进化膜翅目线粒体基因组的特征与进化及其在系统发育研究中的应用

Molecular Phylogeny and Coevolution of Chinese Gallnut Aphid and Its Host-plant Inferred from DNA Sequences Characterization and Evolution of Hymenopteran Mitochondrial Genomes and Their Phylogenetic Utility

MAS中基于协同进化的学习

Co-operative Co-evolution Based Learning in Multi-Agent System

本章重点介绍了最早由Farmer提出的微分方程，基于基因库的模型和协同进化算法模型。

Farmer 's differential equation model , the model based on genes libraries and the co-evolutionary model are mainly discussed in this chapter .

一种基于协同进化的FNN结构优化和集成

Structure Optimization and Ensemble of FNN Based on Cooperative Coevolution

针对蚁群优化算法（Antcolonyoptimization，ACO）收敛速度慢、易陷于局部最优解的缺点，提出了一种基于协同进化思想的蚁群算法，用于求解TSP问题。

Ant Colony Optimization algorithm ( ACO ) has the limitations of poor convergence , and is easy to fall in local optima .

分布式协同进化MDO算法及其在导弹设计中应用

Multidisciplinary design optimization based on distributed coevolution-algorithm and application in missile design

导弹总体参数优化设计的合作协同进化MDO算法

Cooperative Coevolutionary Multidisciplinary Design Optimization of Missile System

将协同进化遗传算法（CGA）应用于图像增强的模糊算法优化，以优化模糊逻辑处理效果。

Co-Genetic Algorithm ( CGA ) is applied to optimization for fuzzy image processing .

本文基于MAS的理论，探讨了多Agent的再励学习方法和协同进化算法，提出了一种进化的多Agent再励学习算法，该算法应用于分散式和同质结构系统中。

This paper will study Multi-Agent Reinforcement learning method and Co-Evolutionary Algorithm on the basis of MAS . Then propose an Evolutionary Reinforcement learning algorithm for map building using multi-agent mobile robots .

多目标的分布式协同进化MDO算法

Multiobjective Distributed Coevolutionary Multidisciplinary Design Optimization

而且，半数以上的模块具有协同进化（co-evolution）的特征并属于特定的进化年代。

Moreover , over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages .

TSK模糊模型的协同进化学习方法

Method of learning TSK fuzzy model by cooperative coevolution

以协同进化而言，TCRV和IgVH的基因重复率分别为1.7×10~（-6）和1.6×10~（-6）/基因年。

As for concerted evolution , gene duplicate rates in TCR V and Ig VH are 1.7 × 10-2 and 1.6 X 10-2 / gene / year , respectively .

协同进化理念下的规划探索与创新&重庆大学与丹麦COBE建筑师事务所合作研究项目的回顾

Programming Exploration and Innovation under the Theory of Co-evolution

协同进化模型已经成功应用于多个领域，如生物学、物理学、化学、经济学、人类学和心理学等，甚至是大规模的NP问题。

Heuristic cooperative model has been successfully used in many fields , such as biology , physics , chemistry , economics , anthropology and psychology , and even large-scale NP problem .

本文针对这两个难点问题，以分解为切入点，利用合作协同进化和双极偏好来分别对变量空间和Pareto前沿进行分解，研究基于分解的多目标粒子群优化算法及其应用。

To solve the two problems , this paper introduce decomposition method in researching multi-objective particle swarm optimization algorithm and apply the cooperative co-evolution and the bipolar preferences to decompose the variable space and the Pareto front .

针对连续型变量与离散型变量的多目标优化问题，分别提出基于博弈策略的多目标粒子群优化算法和面向旅行商问题（TSP）的协同进化粒子群优化算法。

This thesis proposes a multi-objective particle swarm optimization algorithm based on game strategies for continuous multi-objective optimization problem , and a co-evolutionary particle swarm optimization algorithm for Multi-Objective Traveling Salesman Problem ( MOTSP ) with discrete variables .

通过引入非优超排序和排挤的多目标处理机制，将分布式协同进化MDO算法的能力扩展到多目标的多学科设计优化问题。

By introducing multiobjective handling mechanism of nondominated sorting and crowding , ability of distributed coevolutionary multidisciplinary design optimization algorithm is extended to multiobjective multidisciplinary design optimization ( MDO ) problems .

针对神经网络模型可解释能力差的缺点，又提出了基于遗传算法的模糊建模技术，利用基于协同进化的遗传算法对T-S模型的各部分参数进行优化，得到最优的模糊模型。

But for it can 't interpret the model effectively , a new method ( GA-Fuzzy model ) is presented . To get the optimal model , the method optimizes the parameters of T-S fuzzy model use Coevolutionary genetic algorithm .

概括如下：1.引入生态学中的协同进化Lotka-Volterra思想到人工免疫算法中，考虑了群体间的竞争合作关系，构造了一种竞争合作型协同进化免疫克隆选择模型。

Considering the competition and cooperation between populations , the thought of Lotka-Volterra in ecology was introduced into the artificial immune algorithm , a competitive cooperative coevolutionary immune-dominant clone selection algorithm ( CCCICA ) was proposed .

在DCMP方法基本原理和基于协同进化计算的算法实现基础上，针对实际系统的特点，发展基于DCMP方法的相关实用技术。

Based on the principles of the DCMP method and its cooperative co-evolutionary implementation , some practical techniques are developed to enhance its practical feasibility and value .

针对传统遗传算法（TGA）和量子算法（TQA）的优势和不足，借鉴合作型协同进化思想，提出了种群协同进化算法（PCEA）。

By analyzing the standard genetic algorithm ( TGA ) and quantum algorithm ( TQA ), this paper proposes a new algorithm , population co-evolution algorithm ( PCEA ), which utilizes the idea of co-evolution .