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第109行： − + 第117行：
第117行： − + − + − + − + − + − + − − + − − + − + − + − + 第162行：
第160行： − + − + − + − + − + − + − + − + − + − + 第203行：
第201行： − 进化算法+ 第213行：
第211行： − 进化算法是进化计算的一个子集，因为它们通常只涉及实现生物进化机制的技术，如繁殖、变异、重组、自然选择和适者生存。最佳化问题的候选解决方案扮演了人口中个体的角色，而成本函数决定了解决方案“生存”的环境(参见适应度函数)。在重复应用上述算子之后，种群的演化就发生了。+ 第223行：
第221行： − + 第243行：
第241行： − 进化算法和生物学+ 第257行：
第255行： − + 第267行：
第265行： − 然而，算法和信息学的使用，特别是计算理论的使用，超越了对动力系统的类比，也与理解进化本身有关。+ 第277行：
第275行： − 这种观点的优点是认识到发育没有中央控制; 生物体的发育是细胞内部和细胞之间局部相互作用的结果。在我们看来，关于程序开发平行线的最有前途的想法似乎是指向细胞内的进程与现代计算机的低级操作之间明显非常相似的类比。因此，生物系统就像计算机器，处理输入信息来计算下一个状态，这样生物系统比传统的动力系统更接近于计算。+ 第436行：
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无编辑摘要
*[[Ant colony optimization]]
*[[Ant colony optimization]]
蚁群算法
*[[Artificial immune system]]s
*[[Artificial immune system]]s
*[[Artificial life]] (also see [[digital organism]])
*[[Artificial life]] (also see [[digital organism]])
人工生命（参见电子生命）
*[[Cultural algorithm]]s
*[[Cultural algorithm]]s
文化算法
*[[Differential evolution]]
*[[Differential evolution]]
差分演化
*[[Dual-phase evolution]]
*[[Dual-phase evolution]]
双相演化
*[[Estimation of distribution algorithm]]s
*[[Estimation of distribution algorithm]]s
分布算法估计
*[[Evolutionary algorithm]]s
*[[Evolutionary algorithm]]s
演化算法
*[[Evolutionary programming]]
*[[Evolutionary programming]]
演化编程
*[[Evolution strategy]]
*[[Evolution strategy]]
演化策略
*[[Gene expression programming]]
*[[Gene expression programming]]
基因表达式编程算法
*[[Genetic algorithm]]
*[[Genetic algorithm]]
基因算法
*[[Genetic programming]]
*[[Genetic programming]]
基因编程
文法进化
*[[Learnable evolution model]]
*[[Learnable evolution model]]
可学习演化模型
*[[Learning classifier system]]s
*[[Learning classifier system]]s
学习分类系统
*[[Memetic algorithms]]
*[[Memetic algorithms]]
遗传算法
*[[Neuroevolution]]
*[[Neuroevolution]]
神经进化
*[[Particle swarm optimization]]
*[[Particle swarm optimization]]
粒子群优化算法
*[[Synergistic Fibroblast Optimization]]
*[[Synergistic Fibroblast Optimization]]
协作成纤维细胞优化
*[[Self-organization]] such as [[self-organizing map]]s, [[competitive learning]]
*[[Self-organization]] such as [[self-organizing map]]s, [[competitive learning]]
自我管理（例如自组织特征映射模型 竞争性学习）
*[[Swarm intelligence]]
*[[Swarm intelligence]]
集群智能
== Evolutionary algorithms ==
== Evolutionary algorithms ==
演化算法
{{Main|Evolutionary algorithm}}
{{Main|Evolutionary algorithm}}
Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators.
Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators.
演化算法是演化计算的一个子集，因为它们通常只涉及实现生物进化机制的技术，如繁殖、变异、重组、自然选择和适者生存。最佳化问题的候选解决方案扮演了人口中个体的角色，而成本函数决定了解决方案“生存”的环境(参见适应度函数)。在重复应用上述算子之后，种群的演化就发生了。
In this process, there are two main forces that form the basis of evolutionary systems: Recombination mutation and crossover create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality.
In this process, there are two main forces that form the basis of evolutionary systems: Recombination mutation and crossover create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality.
在这个过程中，有两种主要的力量构成了进化系统的基础: 重组变异和交叉创造了必要的多样性，从而促进了新颖性，而选择作为一种力量提高质量。
在这个过程中，有两种主要的力量构成了进化系统的基础: 重组变异和交叉创造了必要的多样性，从而促进了新颖性，而选择作为一种力来提高质量。
== Evolutionary algorithms and biology ==
== Evolutionary algorithms and biology ==
演化算法和生物学
{{Main|Evolutionary algorithm}}
{{Main|Evolutionary algorithm}}
Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.
Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.
遗传算法提供了与动力系统理论相关的生物系统和系统生物学模型的方法，因为它们被用来预测系统的未来状态。这只是一种生动的(但也许是误导性的)方式，提请人们注意生物学发展的有序、控制良好和高度结构化的特征。
遗传算法提供了与动力系统理论相关的生物系统和系统生物学模型的方法，因为它们被用来预测系统的未来状态。这只是一种生动的(但也许是误导性的)方式，提醒人们注意生物学发展的有序、控制良好和高度结构化的特征。
However, the use of algorithms and informatics, in particular of computational theory, beyond the analogy to dynamical systems, is also relevant to understand evolution itself.
However, the use of algorithms and informatics, in particular of computational theory, beyond the analogy to dynamical systems, is also relevant to understand evolution itself.
然而，算法和信息学的使用，特别是计算理论的使用，超越了对动力系统的类比，也与理解演化本身有关。
This view has the merit of recognizing that there is no central control of development; organisms develop as a result of local interactions within and between cells. The most promising ideas about program-development parallels seem to us to be ones that point to an apparently close analogy between processes within cells, and the low-level operation of modern computers. Thus, biological systems are like computational machines that process input information to compute next states, such that biological systems are closer to a computation than classical dynamical system.
This view has the merit of recognizing that there is no central control of development; organisms develop as a result of local interactions within and between cells. The most promising ideas about program-development parallels seem to us to be ones that point to an apparently close analogy between processes within cells, and the low-level operation of modern computers. Thus, biological systems are like computational machines that process input information to compute next states, such that biological systems are closer to a computation than classical dynamical system.
这种观点的优点是认识到发育没有中央控制，生物体的发育是细胞内部和细胞之间局部相互作用的结果。在我们看来，关于程序开发平行线的最有前途的想法似乎是指向细胞内的进程与现代计算机的低级操作之间明显非常相似的类比。因此，生物系统就像计算机器，处理输入信息来计算下一个状态，这样生物系统比传统的动力系统更接近于计算。
* [[Adaptive dimensional search]]
* [[Adaptive dimensional search]]
适应性多维研究
* [[Artificial development]]
* [[Artificial development]]
人工发展
* [[Autoconstructive]]
* [[Autoconstructive]]
自动建设性
* [[Developmental biology]]
* [[Developmental biology]]
发展性生物学
* [[Digital organism]]
* [[Digital organism]]
数字化生物
* [[Estimation of distribution algorithm]]
* [[Estimation of distribution algorithm]]
分布算法估计
* [[Evolutionary robotics]]
* [[Evolutionary robotics]]
演化机器人
* [[Evolved antenna]]
* [[Evolved antenna]]
演化天线
* [[Fitness approximation]]
* [[Fitness approximation]]
适应值近似
* [[Fitness function]]
* [[Fitness function]]
适应值函数
* [[Fitness landscape]]
* [[Fitness landscape]]
适应度景观
* [[Genetic operators]]
* [[Genetic operators]]
遗传算子
* [[Grammatical evolution]]
* [[Grammatical evolution]]
文法演化
* [[Human-based evolutionary computation]]
* [[Human-based evolutionary computation]]
人类演化计算
* [[Inferential programming]]
* [[Inferential programming]]
推断编程
* [[Interactive evolutionary computation]]
* [[Interactive evolutionary computation]]
互动演化计算
* [[List of digital organism simulators]]
* [[List of digital organism simulators]]
数字化有机体模拟器表
* [[Mutation testing]]
* [[Mutation testing]]
变异测试
* [[No free lunch in search and optimization]]
* [[No free lunch in search and optimization]]
研究和优化没有免费的午餐
* [[Program synthesis]]
* [[Program synthesis]]
程序综合
* [[Test functions for optimization]]
* [[Test functions for optimization]]
优化测试函数
* [[Universal Darwinism]]
* [[Universal Darwinism]]
普适达尔文主义