Titlebook: Bioinspired Computation in Combinatorial Optimization; Algorithms and Their Frank Neumann,Carsten Witt Textbook 2010 Springer-Verlag Berlin

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書目名稱Bioinspired Computation in Combinatorial Optimization影響因子(影響力)




書目名稱Bioinspired Computation in Combinatorial Optimization影響因子(影響力)學(xué)科排名




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書目名稱Bioinspired Computation in Combinatorial Optimization被引頻次




書目名稱Bioinspired Computation in Combinatorial Optimization被引頻次學(xué)科排名




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書目名稱Bioinspired Computation in Combinatorial Optimization年度引用學(xué)科排名




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書目名稱Bioinspired Computation in Combinatorial Optimization讀者反饋學(xué)科排名

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Combinatorial Optimization and Computational Complexityetwork of Europe or scheduling exams for given courses at a university. In this chapter, we give a basic introduction to the field of combinatorial optimization. Later on, we discuss how to measure the computational complexity of algorithms applied to these problems and point out some general limita

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Stochastic Search Algorithmsdom decisions, we treat them as randomized algorithms to study their behavior in a rigorous manner. The term . stresses this point of view and will be used in the following to point out that bio-inspired computation methods can be treated as algorithms which are based on random decisions. Mainly we

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Analyzing Stochastic Search Algorithmsbook. We start by describing algorithms for single-objective optimization problems in Section?4.1. There, we consider different variants of RLS and variants of a well-known evolutionary algorithm called (1+1)?EA. Afterwards, we introduce some basic methods methods for analyzing stochastic search alg

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Minimum Spanning Treesa minimum spanning tree in a given undirected connected graph with . vertices and . edges. The problem has many applications in the area of network design. Assume that we have . computers that should be connected with minimum cost, where costs of a certain amount occur when one computer is connected

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Maximum Matchings of the edge set such that no two edges in .′ share a common endpoint. The maximum matching problem asks for a matching of maximum cardinality. Such problems arise, e.g., in team planning when edges of a graph denote possible collaborations of workers and the aim is to find a biggest partition of th

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Shortest Paths.,.) where .={..,…,..} is a set of . vertices and . is a set of . edges. In addition, there is a weight function .:.→? which assigns positive integer weights to the edges. We denote by ..=max?..(.) the maximum of the weights of all edges and distinguish between two problems. In the single-source sho

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Eulerian Cycleshtforward, and it has a large impact on the success of stochastic search algorithms. The Eulerian cycle problems is the simplest problem belonging to the wide class of arc routing problems, and we consider this problem as an example of how the choice of the representation influences the runtime of s

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