Multiobjective Problem Solving from Nature
To those unfamiliar with the field of evolutionary computation (EC), its problem-solving achievements must seem as magical, nearly, as the products of natural evolution itself. Air traffic control in four dimensions and robot teams that perform co-operative navigation; billion-transistor microchi...
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| Main Authors: | , , , |
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| 格式: | 圖書 |
| 語言: | English |
| 出版: |
Springer
2017
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| 主題: | |
| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/27258 |
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| 總結: | To those unfamiliar with the field of evolutionary computation (EC), its
problem-solving achievements must seem as magical, nearly, as the products
of natural evolution itself. Air traffic control in four dimensions and robot
teams that perform co-operative navigation; billion-transistor microchips and
expert-level poker playing: these are not the future, but just some of the past
trophies of the computer scientist’s version of descent with modification.
Of course, behind these achievements lurks some human ingenuity, and
liberal amounts of human perspiration. Practitioners of EC know that it does
not do its magic at the mere twitch of a wand — and there is much work still
ahead to understand how the next step-changes in capability will be reached.
But it remains true that EC demands relatively little from the practitioner
in order to function with at least moderate success. Three ingredients, only,
are needed: a way to express a solution as a data-structure, a way to modify
instances of that data-structure, and a way to calculate the relative quality
of two solutions. These are often simple things to design and implement, and
consequently EC enjoys the labels ‘generic’ and ‘flexible’, able to tackle a huge
diversity of problems. |
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