Algorithms sequential and parallel: a unified approach - download pdf or read online

By Miller R., Boxer L.

For a one-semester, junior/senior-level path in Algorithms. Attuned to the swiftly altering panorama in laptop expertise, this designated and intensely revolutionary textual content is helping scholars comprehend the applying and research of algorithmic paradigms to either the conventional sequential version of computing and to various parallel models-offering a unified, totally built-in assurance of either version forms in order that scholars can discover ways to realize how resolution ideas could be shared between laptop paradigms and architectures

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2). 2. Methods for adaptation of mutation parameters (see Sect. 3). 3. Methods for avoiding function evaluations (see Sect. 4). 1 Development Strands of Modern Evolution Strategies 49 Fig. 2 Restart Heuristics In the case of multimodal objective functions, evolution strategies like any other optimization algorithm run the risk of getting trapped in a local optimum. In such a situation, restart heuristics can provide a decent approach for identifying a number of different local optima through restarts and then choosing the best of the local optima found.

1. Even though this list is still not complete, it contains a few more algorithms than we described in Chap. 2. , approaches to approximating the fitness function topology by means of data-driven modeling algorithms), namely the lmm-CMA-ES, nlmm-CMA-ES, and p-sep-lmm-CMAES are mentioned in the list due to the fact that these algorithms are directly derived from the CMA-ES variants. , these algorithms are neither described in Chap. 2, nor are they taken into account in the empirical comparison presented in Chap.

2. , approaches to approximating the fitness function topology by means of data-driven modeling algorithms), namely the lmm-CMA-ES, nlmm-CMA-ES, and p-sep-lmm-CMAES are mentioned in the list due to the fact that these algorithms are directly derived from the CMA-ES variants. , these algorithms are neither described in Chap. 2, nor are they taken into account in the empirical comparison presented in Chap. 4. However, their fundamental principles are briefly described in Sect. 4. Towards our goal of providing a taxonomy, Fig.

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