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Evolutionary algorithms in theory and practice

Thomas Bäck
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.
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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.

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Proceedings ArticleDOI

Energy aware compilation for DSPs with SIMD instructions

TL;DR: This paper presents compiler optimizations with the aim of minimizing energy consumption of embedded applications that comprises loop optimizations for exploitation of SIMD instructions and zero overhead hardware loops in order to increase performance and decrease the energy consumption.
Journal ArticleDOI

ENPDA: an evolutionary structure-based de novo peptide design algorithm

TL;DR: This work proposes an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch, which was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.
Journal ArticleDOI

Inverse problem based differential evolution for efficient structural health monitoring of trusses

TL;DR: DE search performance for structural damage detection can be considerably improved by integrating RBF into its procedure, and the new algorithm is the best for all test problems.
Journal ArticleDOI

Optimization and analysis aid via data-mining for simulated production systems

TL;DR: The author proposes a methodology which is based on the synergy between evolutionist optimization and an induction graph learning method, which is illustrated via the study of a simulated job-shop composed of five workstations, one entry station and one exit station.
Journal ArticleDOI

A Robust Evolutionary Algorithm for Training Neural Networks

TL;DR: Experimental results indicate that the new approach, called the Family Competition Evolutionary Algorithm (FCEA), is able to stably solve problems, and is very competitive with the comparative evolutionary algorithms.