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MultiObjective Optimization using Evolutionary Algorithms

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real--world search and optimization;

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Multiobjective Optimization Methodology

. Offering insight into multiobjective optimization, the authors show how jumping gene algorithms are a useful addition to existing evolutionary;

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Multiobjective Optimization Methodology

. Offering insight into multiobjective optimization, the authors show how jumping gene algorithms are a useful addition to existing evolutionary;

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Constraint-Handling in Evolutionary Optimization

the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired;

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Computational Intelligence in Expensive Optimization Problems

practice of using computational intelligence for expensive optimization problems.;

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Electromagnetic Optimization By Genetic Algorithms

optimization methodology that allows a global search of the cost surface via statistical random processes dictated by the Darwinian evolutionary;

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Parallel Problem Solving from Nature PPSN XVI

artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world;

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Parallel Problem Solving from Nature PPSN XVI

artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world;

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Multimodal Optimization by Means of Evolutionary Algorithms

, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he;

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Multi-Objective Optimization Using Evolu

. Evolutionary algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. Many of these problems have;

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Applied Evolutionary Algorithms for Engineers using Python

sound starting point for those intending to apply evolutionary algorithms to optimization problems in their working fields. Python has been;

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Classical and Evolutionary Algorithms in the Optimization of Optical Systems

Classical and Evolutionary Algorithms in the Optimization of Optical Systems presents research results in the field of classical and;

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Evolutionary Data Clustering

-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering;

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Emerging Evolutionary Algorithms for Antennas and Wireless Communications

emerging EAs and their variants. Chapter 1 introduces the optimization methods in general and the evolutionary algorithms. Chapter 2 presents;

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Foundations of Computational Intelligence 03

(example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary;

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Extremal Optimization

-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors' recent;

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Evolutionary Algorithms and Neural Networks

optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as;

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Parallel Problem Solving from Nature -- PPSN XIV

included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary;

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Nonlinear Multiobjective Optimization

Arguably, many industrial optimization problems are of the multiobjective type. The present work, after providing a survey of the state;

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Fuzzy Sets and Interactive Multiobjective Optimization

approaches to decision-making under constraints is multiobjective optimization as a generalization of traditional single-objective optimization;

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Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several;

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Evolutionary Computation

- and multi-objective optimization, and then goes on to discuss to explore applications of evolutionary algorithms for many uses with real-world;

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Evolutionary Computation

- and multi-objective optimization, and then goes on to discuss to explore applications of evolutionary algorithms for many uses with real-world;

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Genetic and Evolutionary Methods

Genetic and Evolutionary Methods is a compendium of articles and papers that were presented at GEM '14, an international conference that;

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Multiobjective Optimization

, multiobjective optimization has emerged as a rapdily growing area. In this monograph the author draws from the more mature body of literature on;

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Evolutionary Algorithms For Mobile Ad Hoc Networks

involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting;

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Evolutionary Optimization Algorithms

with a firm grasp of the basic principles of EAs. Covering the theory, history, mathematics, and applications of evolutionary optimization;

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