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第25行： − 进化计算技术可以在大范围的问题设置中产生高度优化的解决方案，这使得它们在计算机科学中受欢迎。存在许多变体和扩展，适合于更具体的问题和数据结构的家族。进化计算有时也用于进化生物学，作为一个硅胶实验过程，以研究一般进化过程的共同方面。+ 第52行：
第51行： − 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。+ 第72行：
第71行： − 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。+ 第147行：
第146行： − 文法进化+ 第198行：
第197行： − 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。+ 第208行：
第207行： − 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。+ 第218行：
第217行： − 这种进化过程的许多方面都是随机的。由于重组和突变而改变的信息片段是随机选择的。另一方面，选择运算符可以是确定性的，也可以是随机的。在后一种情况下，适合度较高的个体比适合度较低的个体有更高的机会被选中，但通常即使是体质较弱的个体也有机会成为父母或生存下来。+ 第252行：
第251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，也与理解演化本身有关。+ 第262行：
第261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272行：
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无编辑摘要
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。用技术术语来讲，它们是一个基于种群'''反复试验trial and error''' 并具有亚启发式或随机优化性质的问题解决算法族。
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。
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。
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。
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.
演化计算技术可以在广泛的问题设置中产生高度优化的解决方案，使其在计算机科学中广受欢迎。演化计算存在许多变体和扩展，能适用于更具体的问题族和数据结构。演化计算有时也被用在演化生物学中，作为一种电子实验程序来研究一般演化过程的共同方面。
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。
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。
文法演化
*[[Learnable evolution model]]
*[[Learnable evolution model]]
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演化机制的技术，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演化系统的基础: 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。
Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive.
Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive.
这种进化过程的许多方面都是随机的。由于重组和突变而改变的信息片段是随机选择的。另一方面，选择运算符可以是确定性的，也可以是随机的。在后一种情况下，适合度较高的个体比适合度较低的个体有更高的机会被选中，但通常即使是体质较弱的个体也有机会成为父本或生存下来。
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，这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.
这一观点的优点是认识到没有发育的中央控制；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，关于程序开发并行的最有前途的想法似乎是那些指出细胞内的进程与现代计算机的低级操作之间明显相似的思想。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，这样生物系统比经典的动力系统更接近于计算。
Furthermore, following concepts from computational theory, micro processes in biological organisms are fundamentally incomplete and undecidable (completeness (logic)), implying that “there is more than a crude metaphor behind the analogy between cells and computers.
Furthermore, following concepts from computational theory, micro processes in biological organisms are fundamentally incomplete and undecidable (completeness (logic)), implying that “there is more than a crude metaphor behind the analogy between cells and computers.
此外，根据计算理论的概念，生物有机体中的微进程从根本上来说是不完整的和不可判定的(完整性(逻辑)) ，这意味着“细胞和计算机之间的类比背后不只是一个粗略的比喻。
此外，根据计算理论的概念，生物有机体中的微进程从根本上来说是不完整的和不可判定的(完整性(逻辑)) ，这意味着细胞和计算机之间的类比背后不只是一个粗略的比喻。
Evolutionary automata, a generalization of Evolutionary Turing machines, have been introduced in order to investigate more precisely properties of biological and evolutionary computation. In particular, they allow to obtain new results on expressiveness of evolutionary computation. This confirms the initial result about undecidability of natural evolution and evolutionary algorithms and processes. Evolutionary finite automata, the simplest subclass of Evolutionary automata working in terminal mode can accept arbitrary languages over a given alphabet, including non-recursively enumerable (e.g., diagonalization language) and recursively enumerable but not recursive languages (e.g., language of the universal Turing machine).
Evolutionary automata, a generalization of Evolutionary Turing machines, have been introduced in order to investigate more precisely properties of biological and evolutionary computation. In particular, they allow to obtain new results on expressiveness of evolutionary computation. This confirms the initial result about undecidability of natural evolution and evolutionary algorithms and processes. Evolutionary finite automata, the simplest subclass of Evolutionary automata working in terminal mode can accept arbitrary languages over a given alphabet, including non-recursively enumerable (e.g., diagonalization language) and recursively enumerable but not recursive languages (e.g., language of the universal Turing machine).
进化自动机是进化图灵机<font color="#ff8000"> 图灵机Turing machines</font>的一种推广，为了更精确地研究生物和进化计算的性质，人们引入了它。特别是，他们允许在进化计算的表现力上获得新的结果。这证实了关于自然进化和进化算法及过程不可判定性的初步结果。进化有限自动机是进化自动机中最简单的子类，在终端模式下可以接受给定字母表上的任意语言，包括非递归的可枚举语言(例如，对角化语言)和递归的可枚举但不递归语言(例如，通用图灵机语言)。
进化自动机是进化图灵机<font color="#ff8000"> 图灵机Turing machines</font>的一种推广，为了更精确地研究生物和演化计算的性质，人们引入了它。特别是，他们允许在演化计算的表现力上获得新的结果。这证实了关于自然演化和演化算法及过程不可判定性的初步结果。演化有限自动机是演化自动机中最简单的子类，在终端模式下可以接受给定字母表上的任意语言，包括非递归的可枚举语言(例如，对角化语言)和递归的可枚举但不递归语言(例如，通用图灵机语言)。
* [[Association for Computing Machinery|ACM]] [[Genetic and Evolutionary Computation Conference]] (GECCO),
* [[Association for Computing Machinery|ACM]] [[Genetic and Evolutionary Computation Conference]] (GECCO),
计算机械协会 遗传与进化计算会议
* [[IEEE Congress on Evolutionary Computation]] (CEC),
* [[IEEE Congress on Evolutionary Computation]] (CEC),
IEEE演化计算大会
* [[EvoStar]], which comprises four conferences: EuroGP, EvoApplications, EvoCOP and EvoMUSART,
* [[EvoStar]], which comprises four conferences: EuroGP, EvoApplications, EvoCOP and EvoMUSART,
EvoStar，包括四个会议：EuroGP、EvoApplications、EvoCOP和EvoMUSART，
* Parallel Problem Solving from Nature (PPSN).
* Parallel Problem Solving from Nature (PPSN).
自然并行问题解决