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

By Chang Wook Ahn

Each real-world challenge from monetary to medical and engineering fields is eventually faced with a typical job, viz., optimization. Genetic and evolutionary algorithms (GEAs) have frequently completed an enviable good fortune in fixing optimization difficulties in quite a lot of disciplines. The objective of this e-book is to supply potent optimization algorithms for fixing a huge category of difficulties quick, appropriately, and reliably through utilising evolutionary mechanisms. during this regard, 5 major concerns were investigated: * Bridging the distance among thought and perform of GEAs, thereby delivering useful layout directions. * Demonstrating the sensible use of the recommended street map. * supplying a great tool to seriously improve the exploratory energy in time-constrained and memory-limited purposes. * supplying a category of promising techniques which are able to scalably fixing tough difficulties within the non-stop area. * establishing a huge tune for multiobjective GEA examine that depends upon decomposition precept. This publication 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 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.

This is in contrast to the scheme adopted in Munetomo’s algorithm [79]. Each partial route is exchanged and assembled, eventually leading to two new routes. It is possible that loops are formed during crossover. In this regard, a simple countermeasure must be prepared with a view to avoiding degenerating the rate of convergence and the quality of solution. Of course, such chromosomes (viz. routes with loops) will gradually be weeded out in the course of a few generations because the traits of those chromosomes drive fitness values from bad to worse.

This is in contrast to the scheme adopted in Munetomo’s algorithm [79]. Each partial route is exchanged and assembled, eventually leading to two new routes. It is possible that loops are formed during crossover. In this regard, a simple countermeasure must be prepared with a view to avoiding degenerating the rate of convergence and the quality of solution. Of course, such chromosomes (viz. routes with loops) will gradually be weeded out in the course of a few generations because the traits of those chromosomes drive fitness values from bad to worse.

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