By Chang Wook Ahn
Each real-world challenge from fiscal to clinical and engineering fields is eventually faced with a standard job, viz., optimization. Genetic and evolutionary algorithms (GEAs) have usually completed an enviable good fortune in fixing optimization difficulties in a variety of disciplines. The aim of this e-book is to supply powerful optimization algorithms for fixing a wide type of difficulties fast, thoroughly, and reliably through making use of evolutionary mechanisms. during this regard, 5 major concerns were investigated: * Bridging the space among conception and perform of GEAs, thereby delivering useful layout guidance. * Demonstrating the sensible use of the steered highway map. * supplying a useful gizmo to noticeably improve the exploratory energy in time-constrained and memory-limited purposes. * delivering a category of promising tactics which are in a position to scalably fixing tough difficulties within the non-stop area. * commencing an incredible song for multiobjective GEA study that is dependent 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
2 Each experiment is terminated when all the chromosomes have converged to the same solution. A convergence test for termination is performed before applying mutation as otherwise uninvited evolution may follow due to the placement of the mutation operator. However, this strategy does not aﬀect the results. Each solution is compared with Dijkstra’s SP  solution. In other words, Dijkstra’s algorithm provides a reference point. Furthermore, the accuracy and the scalability of the population-sizing model are also veriﬁed through simulation studies.
The evolutionary checkers player coevolved with a fully connected feed forward neural network with an input layer, two hidden layers, and an output node  provides a good example in this regard. It needs a tiny population of only 30 to evolve thousands of weights of the neural network. 3 Practical Population-Sizing Model 19 It must be noted that Eq. 14) does not require any knowledge of signal and noise which may not be available in advance in most practical problems. Instead, the model approximates such stochastic information for all the selection mechanisms.
Fig. 5. Veriﬁcation of the population-sizing model for deceptive problems. The results for deceptive problems are shown in Fig. 5. It is also observed that the analytical model is consistent with the experimental results even for higher population size. Moreover, the close agreement between the practical population-sizing model and Harik’s model implies that the proposed decision 22 2 Practical Genetic Algorithms model can accurately approximate the actual SNR without any statistical information about the signal and variance of BBs.