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(\sqrt N) while those of exhaustive search by a traditional computer are O(N) where N is the number of search points."