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Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming.

In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing.

It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool.

This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems. About the Author K. Permissions Request permission to reuse content from this site. Citation lists with outbound citation links are available to subscribers only.

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Table of Contents

Allow All Cookies. Dourado-Sisnando, L.

About this book

Not Accessible Your account may give you access. Abstract Power coupling between different size waveguides has been successfully and efficiently designed and optimized by using evolutionary algorithms based on the artificial immune system and differential evolution in conjunction with the finite element method. References You do not have subscription access to this journal.

Latin America Optics and Photonics Conference At each iteration, new candidate solutions are generated iteratively by taking into consideration the quality of models produced by previous candidate solutions. The rules for the generation of candidate solutions are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. The method is used for the estimation of homographies, considering synthetic and real images, and it is also employed for position estimation in a humanoid robot. Aliev and K. Memmedova uses Z -number based fuzzy approach for modelling the effect of Pilates exercises on motivation, attention, anxiety, and educational achievement of students.

The grade point average of the students was used as the measure of educational achievement. The inference techniques for approximate reasoning based on Z -interpolation method suggested by Zadeh are used in the decision making process. The basic steps of Z -number based modelling with numerical solutions are presented. Martin and J. Reggia considers the optimizing of a neural network's weights and topology using integration of self-assembly SA and particle swarm optimization PSO.

The authors developed a model that integrates network self-assembly and particle swarm optimization for the purpose of growing neural networks with weights and topologies that are optimized for specified computational tasks. The presented model is used for optimizing echo state network weights and topologies on a number of challenging benchmark problems from the domains of time-series forecasting and control.

Wang et al.

Designing neural networks through neuroevolution | Nature Machine Intelligence

A disturbance operation is added to the algorithm in order to make a more careful search near the bird's nests location. The repeat-cycle asymptotic mode is to narrow the disturbance scope based on the last disturbance results and then go on the next disturbance. The proposed algorithm improves convergence velocity and optimization accuracy of the cuckoo search CS algorithm for solving the function optimization problems. This improved algorithm overcomes the CS algorithm's defects which result from its high degree of random and strong leap and also makes full use of the information near the bird's nest location that had been found.

The comparative results with the benchmarking functions show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy. Vijendra and S. Laxman presents a multiobjective genetic clustering approach, in which data points are assigned to clusters based on new line symmetry distance. The proposed multiobjective line symmetry based genetic clustering MOLGC algorithm evolves near-optimal clustering solutions using multiple clustering criteria, without a priori knowledge of the actual number of clusters.

The multiple randomized dimensional trees based nearest neighbour search is used to reduce the complexity of finding the closest symmetric points. Experimental results show that proposed algorithm can obtain optimal clustering solutions in terms of different cluster quality measures.

Advances in Evolutionary Computing for System Design (Studies in Computational Intelligence)

Abiyev and M. Tunay proposes a novel evolutionary learning algorithm based on evaluation and optimization of a hypercube for solving global numerical optimization problems. The algorithm is inspired from the behaviour of doves discovering new areas for food in natural life. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process.

The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube.


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The displacement-shrink process determines displacement and evaluates objective functions using new points; the search area process determines next hypercube using certain rules and evaluates the new solutions. The HO algorithm is tested on a set of specific benchmarking functions and has shown better performance for global optimization of both low- and high-dimensional problems with large numbers of local optimal.

Su et al.

Advances in Evolutionary Algorithms Research Computer Science, Technology and Applications

For this purpose, the design of fitness function that satisfies the equivalence between the optimal solution and the minimal attribute reduction is considered.