《The Master Algorithm》的笔记-Ch V. Evolution: Nature's Learning Algorithm
- 章节名：Ch V. Evolution: Nature's Learning Algorithm
- 2018-02-24 22:20:24
...the stage was set for the second coming of evolution: in silico instead of in vivo, and a billion times faster. [...] The key input to a genetic algorithm, as Holland's creation came to be known, is a fitness function. Given a candidate program and some purpose it is meant to fill, the fitness function assigns the program a numeric score reflecting how well it fits the purpose.
是不是听起来很熟悉，像深蓝也像AlphaNotice how uch genetic algorithms differ from multilayer perceptrons. Backprop entertains a single hypothesis at any given time, and the hypothesis changes gradually until it settles into a local optimum. Genetic algorithms consider an entire population of hypotheses at each step, and these can make big jumps from one generation to the next, thanks to crossover. Backprop proceeds deterministically after setting the initial weights to small random values. Genetic algorithms, in contrast, are full of random choices: which hypotheses to keep alive and cross over (with fitter hypotheses being more likely candidates), where to cross two strings, which bits to mutate. Backprop learns weights for a predefined network architecture; denser networks are more flexible but also harder to learn. Genetic algorithms make no a priori assumptions abot the structures they will learn, other than their general form.
从单一假设扩张到多个随机假设，思考力不是不被需要了，反而更加重要：因为如何保证一开始设定的building blocks就要相对有意义？The better a slot machine looks, the more you should play it, but never completed give up on the other one, in case it turns out to be the best one after all.
Find the most intersting thing to devote your energy, time and resources (money) but never give it all you have and open for other things that could come later that looks even more interesting?
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Ch V. Evolution: Nature's Learning Algorithm
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