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第1行： − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。{{for|the journal|Evolutionary Computation (journal)}}+ 第19行：
第19行： − 在计算机科学中，进化计算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，他们是一个家庭的基于人口试验和错误的问题解决与亚启发式或随机优化性质。+ 第29行：
第29行： − 在进化计算中，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溶液，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，种群将逐渐演化以增加适应度，在这种情况下，选择适应度函数的算法。+ 第55行：
第55行： − 自动化问题解决的进化原理的使用起源于20世纪50年代。直到20世纪60年代，才在三个不同的地方形成了对这一观点的三种不同的解释。+ 第65行：
第65行： − 进化规划是由美国的劳伦斯 · j · 福格尔提出的，而约翰 · 亨利 · 霍兰德称他的方法为遗传算法。在德国，Ingo Rechenberg 和 Hans-Paul Schwefel 引入了进化策略。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进化计算的技术的不同代表(“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进化计算。+ 第75行：
第75行： − 这些术语表示进化计算领域，并将进化规划、进化策略、遗传算法和遗传规划作为子领域。+ 第85行：
第85行： − 利用进化算法和人工生命模拟进化，始于20世纪60年代尼尔斯 · 阿尔 · 巴里切利(Nils Aall Barricelli)的工作，后来被亚历克斯 · 弗雷泽(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 使用进化策略解决复杂的工程问题，人工进化因此成为广泛认可的优化方法。遗传算法尤其通过约翰 · 霍兰德的著作而流行起来。随着学术兴趣的增长，计算机能力的戏剧性增长允许实际应用，包括计算机程序的自动进化。进化算法现在被用来解决多维问题，比人类设计师开发的软件更有效率，也可以用来优化系统设计。+ 第96行：
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无编辑摘要
此词条暂由Henry翻译。{{for|the journal|Evolutionary Computation (journal)}}
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，演化计算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。用技术术语来讲，他们是一个基于人口试验和错误的问题解决并具有亚启发式或随机优化性质的家族。
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，种群将逐渐演化以增加适应度，在这种情况下，选择适应度函数的算法。
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.
自动化问题解决的演化原理的使用起源于20世纪50年代。直到20世纪60年代，才在三个不同的地方形成了对这一观点的三种不同的解释。
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.
进化规划是由美国的 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 引入了进化策略。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。
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.
这些术语表示演化计算领域，并将演化规划、演化策略、遗传算法和遗传规划作为子领域。
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，人工进化因此成为广泛认可的优化方法。遗传算法尤其通过John Holland的著作而流行起来。随着学术兴趣的增长，计算机能力的戏剧性增长允许其实际应用，包括计算机程序的自动进化。演化算法现在被用来解决多维问题，比人类设计师开发的软件更有效率，它也可以用来优化系统设计。
== Techniques ==
== 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:
*[[Artificial immune system]]s
*[[Artificial immune system]]s
人工免疫系统
*[[Artificial life]] (also see [[digital organism]])
*[[Artificial life]] (also see [[digital organism]])