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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.
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.

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Citations
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Journal ArticleDOI

A hybrid genetic approach for channel reuse in multiple access telecommunication networks

TL;DR: The proposed method is based on a hybrid genetic algorithm and aims to accomplish the establishment of a maximal number of connections with the minimal number of isochronous channels, revealing the robustness, the flexibility and the efficiency of using evolutionary approaches to complex real-world problems.
Journal ArticleDOI

An efficient implementation of parallel simulated annealing algorithm in GPUs

TL;DR: A highly optimized version of a simulated annealing (SA) algorithm adapted to the more recently developed graphic processor units (GPUs) with CUDA toolkit, specially designed for Nvidia GPUs is proposed.
Journal ArticleDOI

Optimizing survivability of vulnerable series–parallel multi-state systems

TL;DR: An algorithm based on the universal generating function method is suggested for determination of the vulnerable series–parallel multi-state system survivability and a genetic algorithm is used as optimization tool in order to solve the structure optimization problem.
Proceedings ArticleDOI

On evolution strategy optimization in dynamic environments

TL;DR: The article analyzes the behavior of evolution strategies and their current mutation variants on a simple rotating dynamic problem and finds the complex covariance matrix adaptation proves to be superior with slow rotation but with increasing dynamism whose adaptation mechanism seldom finds the optimum where the simple uniform adaptation produces stable results.
Journal ArticleDOI

Optimization and Blending of Composite Laminates Using Genetic Algorithms with Migration

TL;DR: The objective function evaluating the fitness of designs is modified according the severity of mismatches detected between neighboring populations, laying the groundwork for natural evolution to a blended global solution without leaving the paradigm of genetic algorithms.