Open AccessBook
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
Citations
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Proceedings ArticleDOI
A multi-objective genetic algorithm for on-chip real-time optimisation of word length and power consumption in a pipelined FFT processor targeting a MC-CDMA receiver
Nasri Sulaiman,Tughrul Arslan +1 more
TL;DR: This paper presents a multi-objective genetic algorithm for on-chip real-time optimisation of word length and power consumption in a fixed-point pipelined fast Fourier transform (FFT) processor targeting on MC-CDMA receiver.
Proceedings ArticleDOI
Self-Adaptive Mutation in On-line, On-board Evolutionary Robotics
TL;DR: It is argued that the most influential factor to govern evolution in the authors' application is the mutation operator, and to address the problem of adjusting its parameter(s), different on-line parameter control mechanisms are identified and an experimental comparison among them.
Journal Article
Variable Mutation Rate at Genetic Algorithms: Introduction of Chromosome Fitness in Connection with Multi-Chromosome Representation
TL;DR: This paper investigates the effects on GA performance or the optimization results by balancing control parameters to the fitness of a chromosome (chromosome fitness).
Differential evolution based classification with pool of distances and aggregation operators
TL;DR: This thesis develops and generalizes further the differential evolution based data classification method and demonstrates that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy.