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Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Journal ArticleDOI
Robust Evolution Strategies
TL;DR: It is demonstrated that a population cannot practically move to other better points, because strategy parameters attain minute values at an early stage, when too small a lower bound is adopted.
Proceedings ArticleDOI
Metamodel-assisted mixed integer evolution strategies and their application to intravascular ultrasound image analysis
Rui Li,Michael Emmerich,Jeroen Eggermont,Ernst G. P. Bovenkamp,Thomas Bäck,Jouke Dijkstra,Johan H. C. Reiber +6 more
TL;DR: Experimental results, both on artificial test problems and a real world application, indicate a clear acceleration that can be achieved by using heterogeneous RBFN.
Journal ArticleDOI
Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide
TL;DR: A framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes is introduced, and a systematic evaluation of the performance of frequently used metamodeling techniques is presented.
Journal ArticleDOI
Optimization of Distributed Raman Amplifiers Using a Hybrid Genetic Algorithm With Geometric Compensation Technique
TL;DR: An accurate method that combines a hybrid genetic algorithm (GA) with a geometric compensation technique applied to an analytical Raman amplifier model to obtain the optimal design of multipump distributed Raman amplifiers is proposed.
Journal ArticleDOI
Convergence in Evolutionary Programs with Self-Adaptation
TL;DR: A modified version of the 1/5-success rule for self-adaptation in evolution strategies (ES) is proposed and preliminary tests indicate an ES with the modified self- Adaptation method compares favorably to both a non-adapted ES and a 1/ 5- success rule adapted ES.