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In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

在计算机科学中，<font color="#ff8000"> 演化计算 Evolutionary computation</font>是一个受生物进化启发的全局优化算法家族，人工智能和软计算的子领域研究这些算法。在技术术语上，它们是一个基于群体的试错问题求解器家族，具有元启发式或随机优化特性。

在计算机科学中，<font color="#ff8000"> 演化计算 Evolutionary computation</font>是一个受生物演化启发的全局优化算法家族，这些算法的研究属于人工智能和软计算的子领域。在技术术语上，它们是一类基于群体的试错型问题求解器，具有元启发式或随机优化特性。

In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm.

In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm.

在演化计算中，一个初始的候选解决方案集被生成并迭代更新。每一代都是通过随机去除不太理想的解法，引入小的随机变化而产生的。在生物学术语中，一个解决方案的群体经历自然选择(或人工选择)和突变。因此，种群会逐渐演化为适应度增加，在这种情况下选择适应度函数的算法。

在演化计算中，一个初始的候选解决方案集被生成并迭代更新。每一代都是通过随机去除不太理想的解法，引入小的随机变化而产生的。在生物学术语中，一个解决方案的群体会经历自然选择(或人工选择)和突变。因此，种群会逐渐演化，其适应度不断提高，在这个语境中所谓适应度就是算法选择的目标函数。

Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes.

Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes.

== History ==

== 历史 ==

 

The use of evolutionary principles for automated problem solving originated in the 1950s. It was not until the 1960s that three distinct interpretations of this idea started to be developed in three different places.

The use of evolutionary principles for automated problem solving originated in the 1950s. It was not until the 1960s that three distinct interpretations of this idea started to be developed in three different places.

The use of evolutionary principles for automated problem solving originated in the 1950s. It was not until the 1960s that three distinct interpretations of this idea started to be developed in three different places.

The use of evolutionary principles for automated problem solving originated in the 1950s. It was not until the 1960s that three distinct interpretations of this idea started to be developed in three different places.

Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm. In Germany Ingo Rechenberg and Hans-Paul Schwefel introduced evolution strategies. These areas developed separately for about 15 years. From the early nineties on they are unified as different representatives ("dialects") of one technology, called evolutionary computing. Also in the early nineties, a fourth stream following the general ideas had emerged – genetic programming. Since the 1990s, nature-inspired algorithms are becoming an increasingly significant part of the evolutionary computation.

Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm. In Germany Ingo Rechenberg and Hans-Paul Schwefel introduced evolution strategies. These areas developed separately for about 15 years. From the early nineties on they are unified as different representatives ("dialects") of one technology, called evolutionary computing. Also in the early nineties, a fourth stream following the general ideas had emerged – genetic programming. Since the 1990s, nature-inspired algorithms are becoming an increasingly significant part of the evolutionary computation.

演化规划是由美国的 Lawrence J. Foge提出的，而 John Henry Holland称他的方法为遗传算法。在德国，Ingo Rechenberg 和 Hans-Paul Schwefel 引入了演化策略。这些地区分别发展了大约15年。从九十年代早期开始，它们被统一为一种被称为演化计算的技术的不同代表(类似“方言”)。也是在九十年代初期，出现了继一般思想之后的第四种思潮——遗传程序设计。自20世纪90年代以来，以自然为灵感的算法正在成为日益重要的演化计算。

<font color="#ff8000">演化程序设计 Evolutionary programming</font>是由美国的 Lawrence J. Foge提出的，而[[约翰·霍兰德_John_H_Holland|约翰·霍兰德]]称他的方法为[[遗传算法]]。在德国，Ingo Rechenberg 和 Hans-Paul Schwefel 引入了<font color="#ff8000">演化策略 evolution strategies</font>。这些领域分别独立地发展了大约15年。从九十年代早期开始，它们被统一为一种被称为演化计算的技术的不同表示(类似“方言”)。也是在九十年代初期，出现了继一般思想之后的第四种思潮——<font color="#ff8000">遗传程序设计 genetic programming</font>。自20世纪90年代以来，以自然为灵感的算法正在成为演化计算日益重要的一部分。

These terminologies denote the field of evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas.

These terminologies denote the field of evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas.

Simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection. Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems. Genetic algorithms in particular became popular through the writing of John Holland. As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.

Simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection. Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems. Genetic algorithms in particular became popular through the writing of John Holland. As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.

利用演化算法和人工生命模拟进化始于20世纪60年代 Nils Aall Barricelli的工作，后来被Alex Fraser扩展，他发表了一系列关于人工选择模拟的论文。20世纪60年代和70年代早期，Ingo Rechenberg 使用演化策略解决复杂的工程问题，人工演化因此成为广泛认可的优化方法。尤其是通过约翰·霍兰德的著作，遗传算法变得流行起来。随着学术兴趣的增长，计算机能力的急剧增长使得这种算法可以实际应用起来，其中包括计算机程序的自动演化。演化算法现在被用来解决多维问题，比人类设计者生产的软件更有效，同时也可以优化系统的设计。

利用演化算法和人工生命模拟进化始于20世纪60年代 Nils Aall Barricelli的工作，后来被Alex Fraser扩展，他发表了一系列关于人工选择模拟的论文。20世纪60年代和70年代早期，Ingo Rechenberg 使用演化策略解决复杂的工程问题，人工演化因此成为被广泛认可的优化方法。尤其是遗传算法，因为[[约翰·霍兰德_John_H_Holland|约翰·霍兰德]]的著作而变得流行起来。学术界兴趣增长的同时，计算机能力的急剧增长使得这种算法可以被实际应用起来，其中包括计算机程序的自动演化。演化算法现在被用来解决多维度问题，且比人类设计者生产的软件更有效，同时还可以优化系统的设计。

== Techniques ==

== 技术 ==

Evolutionary computing techniques mostly involve [[metaheuristic]] [[Mathematical optimization|optimization]] [[algorithm]]s. Broadly speaking, the field includes:

Evolutionary computing techniques mostly involve [[metaheuristic]] [[Mathematical optimization|optimization]] [[algorithm]]s. Broadly speaking, the field includes:

 

*[[蚁群优化算法]]

 

*[[Artificial immune system]]s 人工免疫系统

 

*[[人工生命_Artificial_life|人工生命]] (also see [[digital organism]])

*[[Ant colony optimization]]

*[[Cultural algorithm]]s 文化算法

 

*[[Differential evolution]] 差分演化

*[[Dual-phase evolution]] 双相演化

 

*[[Estimation of distribution algorithm]]s 分布算法估计

*[[Artificial immune system]]s

*[[演化算法_Evolutionary_Algorithms|演化算法]]

 

*[[Evolutionary programming]] 演化编程

人工免疫系统

*[[Evolution strategy]] 演化策略

 

*[[Gene expression programming]] 基因表达式编程算法

*[[Artificial life]] (also see [[digital organism]])

*[[Genetic algorithm]] 遗传算法

 

*[[Genetic programming]] 遗传程序设计

*[[Grammatical evolution]] 文法演化

 

*[[Learnable evolution model]] 可学习演化模型

*[[Cultural algorithm]]s

*[[Learning classifier system]]s 学习分类器系统

文化算法

*[[Memetic algorithms]] 模因算法

 

*[[Neuroevolution]] 神经演化

 

*[[粒子群优化算法]]

*[[Differential evolution]]

*[[Synergistic Fibroblast Optimization]] 协作成纤维细胞优化

 

差分演化

 

*[[Dual-phase evolution]]

 

双相演化

 

*[[Estimation of distribution algorithm]]s

 

分布算法估计

 

*[[Evolutionary algorithm]]s

 

*[[Evolutionary programming]]

 

演化编程

*[[Evolution strategy]]

演化策略

 

 

*[[Gene expression programming]]

 

基因表达式编程算法

 

*[[Genetic algorithm]]

 

 

*[[Genetic programming]]

 

 

*[[Grammatical evolution]]

 

 

文法演化

*[[Learnable evolution model]]

 

可学习演化模型

*[[Learning classifier system]]s

 

 

*[[Memetic algorithms]]

 

 

*[[Neuroevolution]]

 

 

*[[Particle swarm 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）

自我管理（例如自组织特征映射模型 竞争性学习）