演化计算
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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.
在计算机科学领域，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。
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.
在进化计算，一个初始的候选解决方案集生成和迭代更新。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，解决方案的种群经受自然选择(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。
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.
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广受欢迎。存在许多变体和扩展，适合于更具体的问题和数据结构家族。进化计算有时也被用于进化生物学中，作为一种计算机实验过程来研究一般进化过程的共同方面。
History
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开始形成对这一思想的三种不同的解释。
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劳伦斯 · j · 福格尔提出的，而约翰 · 亨利 · 霍兰德则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——遗传编程。自20世纪90年代以来，基于大自然的算法正在成为进化计算的一个重要组成部分。
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.
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。
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.[1] 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.[2] Genetic algorithms in particular became popular through the writing of John Holland.[3] As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.[4] 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.[5][6]
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.
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 · 巴里切利的工作，后来被亚历克斯 · 弗雷泽扩展，他发表了一系列关于人工选择模拟的论文。
Techniques
Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes:
Category:Evolution
分类: 进化
This page was moved from wikipedia:en:Evolutionary computation. Its edit history can be viewed at 演化计算/edithistory
- ↑ Fraser AS (1958). "Monte Carlo analyses of genetic models". Nature. 181 (4603): 208–9. Bibcode:1958Natur.181..208F. doi:10.1038/181208a0. PMID 13504138. S2CID 4211563.
{{cite journal}}: Invalid|ref=harv(help) - ↑ Rechenberg, Ingo (1973) (in German). Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Fromman-Holzboog.
- ↑ Holland, John H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 978-0-262-58111-0. https://archive.org/details/adaptationinnatu00holl.
- ↑ Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. ISBN 978-0-262-11170-6.
- ↑ G. C. Onwubolu and B V Babu, Onwubolu, Godfrey C.; Babu, B. V. (2004-01-21). New Optimization Techniques in Engineering. ISBN 9783540201670. https://www.springer.com/in/book/9783540201670. Retrieved 17 September 2016.
- ↑ Jamshidi M (2003). "Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms". Philosophical Transactions of the Royal Society A. 361 (1809): 1781–808. Bibcode:2003RSPTA.361.1781J. doi:10.1098/rsta.2003.1225. PMID 12952685. S2CID 34259612.
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