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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
An improved genetic algorithm for the multiconstrained 0-1 knapsack problem
TL;DR: An improved hybrid genetic algorithm for solving the multiconstrained 0-1 knapsack problem (MKP) is presented, based on the solution of the LP relaxed MKP, and an efficient pre-optimization of the initial population is suggested.
Journal ArticleDOI
A global optimization algorithm inspired in the behavior of selfish herds.
TL;DR: The experimental results show the remarkable performance of the proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems.
Proceedings ArticleDOI
Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior
TL;DR: The proposed approach combines FCM and ES concepts and sets the basis for the establishment and deployment of structural evolution, which broadens the applicability of FCMs.
Journal ArticleDOI
A dynamic over-sampling procedure based on sensitivity for multi-class problems
TL;DR: A dynamic over-sampling procedure is proposed for improving the classification of imbalanced datasets with more than two classes that is incorporated into a memetic algorithm (MA) that optimizes radial basis functions neural networks (RBFNNs).
Journal ArticleDOI
Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms
Dogan Corus,Pietro S. Oliveto +1 more
TL;DR: In this paper, a Markov chain framework was devised to rigorously prove an upper bound on the runtime of standard steady state GAs to hillclimb the OneMax function.