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A hill-climber, on the other hand, will do much better because of its ability to search many more attraction basins. Of course, it will not be able to find the global optimum, unless it is a very small search space, but it will, simply by brute force, search many more basins, and thus do better. Furthermore, the ability of R B C + to search neighboring basins will give it an advantage over RBC for larger N. Since RBC will only be able to search a small fraction of all the local minima, the ability to search deeper in each region explored will give R B C + an advantage.

2 Observations Examinations of NKP landscapes [10] (of which NK landscapes are a subset, with P=N), revealed the following with regard to peak distribution characteristics: 9 The parameter P has little effect on the most critical of these characteristics. 9 However, P acts as a "gain factor" on the landscape's expected range of fitness values. 9 Moreover, P seems more responsible than K for the so-called complexity catastrophe in these landscapes. A more general observation is that K is an overly sensitive control on peak distribution complexity.

20 1 0 ~ I L 20 1 I 40 60 , I 80 , I 100 F i g u r e 1 Performances for CHC, R B C + , and SGA on NK-landscape problems, treated as minimization problems, where N -- 100. This graph is reproduced using the data given in Heckendorn, et al. [7]. We have added error bars representing 2 9 S E M . 2 THE ALGORITHMS In this work we have begun to characterize the performance niches for random search, CHC using the HUX [3] and two-point reduced surrogate recombination (2X) [1] operators, a random bit-climber, RBC [2], and an enhanced random bit-climber, R B C + [7].

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