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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
A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems
TL;DR: This paper adopts the use of a simulation–optimization approach that couples a numerical pollutant-transport simulation model with evolutionary search algorithms for solution of the inverse problem of groundwater source identification.
Journal ArticleDOI
An alternative approach for neural network evolution with a genetic algorithm: Crossover by combinatorial optimization
TL;DR: A new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator, which is compared to a classical crossover in 25 real-world problems with an excellent performance.
Journal ArticleDOI
Influence of the Migration Policy in Parallel DistributedGAs with Structured and Panmictic Populations
Enrique Alba,José M. Troya +1 more
TL;DR: This paper investigates the influence of migration frequency and migrant selection in a ring of islands running either steady-state, generational, or cellular GAs, and points out the considerable benefits that can accrue from asynchronous migration.
Book ChapterDOI
Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities
TL;DR: This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then makes an informed guess on the number of free parking spaces near to the medium time horizon.
Proceedings ArticleDOI
A comparison of static analysis and evolutionary testing for the verification of timing constraints
Frank Mueller,Joachim Wegener +1 more
TL;DR: The results show that static analysis and evolutionary testing are complementary methods, which together provide upper and lower bounds for both worst-case and best-case execution times.