Download Advances in Evolutionary Algorithms: Theory, Design and by Chang Wook Ahn PDF

By Chang Wook Ahn

ISBN-10: 3540317589

ISBN-13: 9783540317586

Each real-world challenge from fiscal to medical and engineering fields is finally faced with a standard activity, viz., optimization. Genetic and evolutionary algorithms (GEAs) have frequently accomplished an enviable good fortune in fixing optimization difficulties in quite a lot of disciplines. The aim of this booklet is to supply powerful optimization algorithms for fixing a wide category of difficulties speedy, competently, and reliably by means of utilising evolutionary mechanisms. during this regard, 5 major concerns were investigated: * Bridging the space among concept and perform of GEAs, thereby supplying functional layout directions. * Demonstrating the sensible use of the recommended highway map. * delivering a great tool to seriously increase the exploratory energy in time-constrained and memory-limited functions. * supplying a category of promising techniques which are in a position to scalably fixing not easy difficulties within the non-stop area. * establishing an immense tune for multiobjective GEA study that will depend on decomposition precept. This booklet serves to play a decisive function in bringing forth a paradigm shift in destiny evolutionary computation.

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Extra info for Advances in Evolutionary Algorithms: Theory, Design and Practice

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The gene of the first locus encodes the source node, and the gene of second locus is randomly or heuristically selected from the nodes connected with the source node (S) that is represented by the front gene’s allele. The chosen node is removed from the topological information database to prevent the node from being selected twice, thereby avoiding loops in the path. This process continues until the destination node is reached. Note that an encoding is possible only if each step of a path passes through a physical link in the network.

6. Convergence property of each algorithm. converging through smaller generations has better convergence performance because all the algorithms have the same population size in the experiment. In general, however, convergence performance must be compared with the average number of fitness function evaluations until the GAs reach equal quality of solutions [22]. A detailed explanation will be given in Sect. 2. , the optimal route), notwithstanding the somewhat inherent initial disadvantage. , networks types and scales).

The reason is not far to seek: the proposed algorithm involves the smallest number of fitness function evaluations. That means faster convergence. Networks with 15–50 nodes, and randomly assigned link costs were also studied. The results in respect of number of fitness function evaluations are shown in Fig. 8. , maximum difference is about 4%) is smaller than any other algorithm. In case of 30 nodes, for instance, the proposed GA is faster than Inagaki’s GA and Munetomo’s GA with prob. 2. Performance comparison on the rate of convergence.

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