Manual Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)

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Contents

  1. Differential Evolution
  2. 1. Introduction
  3. Differential Evolution: A Practical Approach to Global Optimization | BibSonomy

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Journal of Global Optimization. Differential Evolution: In Search of Solutions. Retrieved 17 September Das and P. Das, S.

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Differential Evolution

Convex programming Integer programming Quadratic programming Nonlinear programming Stochastic programming Robust optimization Combinatorial optimization Infinite-dimensional optimization Metaheuristics Constraint satisfaction Multiobjective optimization. This modification limits the possible premature convergence to local minima and ensures the asymptotic global convergence. We also introduce concepts that are necessary for the subsequent proof of the asymptotic global convergence of the modified algorithm.

We test the classic and modified algorithm by numerical experiments and compare the efficiency of finding the global minimum for both algorithms. The tests confirm that the modified algorithm is significantly more efficient with respect to the global convergence than the classic algorithm. Agoston E.

Download Differential Evolution A Practical Approach to Global Optimization Natural Computing Series

Patrick Siarry. Junghuei Chen. Gabriel Ciobanu. Alex A. Franz Rothlauf. Shigeyoshi Tsutsui. Gisele L. Daniel S.

1. Introduction

Nelishia Pillay. Frank Neumann.

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Differential Evolution: A Practical Approach to Global Optimization | BibSonomy

New Releases. Description Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution DE algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast.