Amanote Research

Amanote Research

    RegisterSign In

On Improving Approximate Solutions by Evolutionary Algorithms

doi 10.1109/cec.2007.4424800
Full Text
Open PDF
Abstract

Available in full text

Date

September 1, 2007

Authors
Tobias FriedrichJun HeNils HebbinghausFrank NeumannCarsten Witt
Publisher

IEEE


Related search

Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2013English

Using Evolutionary Algorithms to Achieve Sustainable Solutions Through Household Appliances

Journal of Modeling and Optimization
2019English

Improving Interpretability of Fuzzy Models Using Multi-Objective Neuro-Evolutionary Algorithms

2008English

Parallel Hybrid Evolutionary Algorithms on GPU

2010English

Faster Evolutionary Algorithms by Superior Graph Representation

2007English

In Search of Proper Pareto-Optimal Solutions Using Multi-Objective Evolutionary Algorithms

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2007English

On Approximate Solutions for Time-Fractional Diffusion Equation

Journal of Asian Scientific Research
2018English

Validating Evolutionary Algorithms on Volunteer Computing Grids

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2010English

Co-Evolutionary Algorithms Based on Mixed Strategy

Journal of Information Technology Research
Computer Science
2011English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy