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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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Proceedings ArticleDOI
A Hybrid Bioinspired Algorithm for Facial Emotion Recognition Using CSO-GA-PSO-SVM
TL;DR: A bio inspired algorithm in conjunction with the support vector machine (SVM) will find an optimal feature set from a bigger set and is applied over the facial characteristics captured from students in teaching-learning environment to demonstrate its effectiveness and efficiency in real-time scenario.
Journal ArticleDOI
Optimal design of ultrasonic motor using evolution strategy and finite element method
TL;DR: In this paper, the authors proposed the optimal design methodology of a USM by using the Evolution Strategy (ES) and the Finite Element Method (FEM) for the L1B4 USM.
Proceedings ArticleDOI
Multi-ring Particle Swarm Optimization
TL;DR: A novel PSO topology based on multiples rings is proposed for improving the results achieved focusing on the diversity provided by the ring rotations, and results have shown that the proposed topology achieves better results than the well known star and ring topologies.
Book ChapterDOI
Evolving Transition Rules for Multi Dimensional Cellular Automata
Ron Breukelaar,Thomas Bäck +1 more
TL;DR: The genetic algorithm principle is extended to multi dimensional CA, and it is demonstrated how the approach evolves transition rules for the two dimensional case with a von Neumann neighborhood by using the GA to optimize the corresponding rules.
Journal ArticleDOI
Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability
Ankit Agrawal,Sarsij Tripathi +1 more
TL;DR: A new improved version of PSO that uses adaptive inertia weight technique which is based on cumulative binomial probability (CBPPSO) which is evaluated on three real-world engineering problems and the results obtained are promising.