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If we want to break someone else's PIN (personal identification number) of, say, an ATM (automated teller machine), how many trials would be necessary when we want to be efficient? This is a sort of what we call a-needle-in-a-hay-stack problem. In 1987, in their wonderful paper, Hinton & Nowlan proposed a genetic algorithm with a needle being a unique configuration of 20-bit binary string while all other configurations being a haystack. What they proposed was to exploit a lifetime learning of individuals in their genetic algorithm, calling it the Baldwin effect in a computer. Since then there has been a fair amount of exploration of this effect, claiming, "this is a-needle-in-a-hay-stack problem, and we've found a more efficient algorithm than a random search." Some of them, however, were found to be the results of an effect of like-to-hear-what-we-would-like-to- hear. In this talk, we will try a bird's eye view on a few examples we have had so far, and how they were explored, including the approach by means of quantum computation which claims, "The steps to find a needle are O(radicN) while those of exhaustive search by a traditional computer are O(N) where N is the number of search points."