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Evolutionary Constrained Optimization

evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based;

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Evolutionary Multi-Criterion Optimization

This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017;

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Constraint-Handling in Evolutionary Optimization

amount of research has been d- icated to design and implement constraint-handling techniques. The use of (exterior) penalty functions is one;

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Artificial Evolution

algorithms...), memetic algorithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning;

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Artificial Evolution

algorithms...), memetic algorithms, multi-objective optimisation, constraint handling, parallel algorithms,, dynamic optimization, machine learning;

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Evolutionary Computation 2

is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the;

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Evolutionary Algorithms

operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are;

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A Brief Introduction to Continuous Evolutionary Optimization

function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive;

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Ant Colony Optimization and Constraint Programming

describes the ant colony optimization metaheuristic and illustrates its capabilities on different constraint satisfaction problems. The third part;

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Optimization Techniques and Applications with Examples

neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization;

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Logic-Based Methods for Optimization

A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization;

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Recent Advances in Computational Optimization

, based on new intelligent methods like evolutionary computations, ant colony optimization and constraint programming, and demonstrates how real;

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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

This book constitutes the refereed proceedings of the Third International Conference on Integration of AI and OR Techniques in Constraint;

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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

This book constitutes the refereed proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint;

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AIDA CMK Multi Algorithm Optimization Kernel Applied to Analog IC Sizing

implementations. The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with;

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Adaptive Stochastic Optimization Techniques with Applications

optimization, decision analysis, intelligent systems, evolutionary programming, heuristic optimization, stochastic and adaptive dynamic programming;

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Constraint Handling Rules - Compilation, Execution, and Analysis

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Stochastic Optimization Techniques

substitute problems, especially the handling of mean value and probability functions as objective and/or constraint functions. Moreover, many;

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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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Stability and Optimization of Structures

This book focuses on the optimization of a geometrically-nonlinear structure under stability constraint. It presents a deep insight into;

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Nature-Inspired Optimization Algorithms

annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid;

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Nature-Inspired Optimization Algorithms

, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling;

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Self-Adaptive Heuristics For Evolutionary Computation

question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer;

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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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Integration of AI and OR Techniques in Constraint Programming for Combinatorial

This book constitutes the refereed proceedings of the 8th International Conference on Integration of AI and OR Techniques in Constraint;

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Principles in Noisy Optimization

of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents;

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Interval Methods for Solving Nonlinear Constraint Satisfaction, Optimization and Similar Problems

This book highlights recent research on interval methods for solving nonlinear constraint satisfaction, optimization and similar problems;

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