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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
Citations
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Journal ArticleDOI
Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach
Ahmad B. A. Hassanat,Khalid Almohammadi,Esra'a Alkafaween,Eman Abunawas,Awni Hammouri,V. B. Surya Prasath +5 more
TL;DR: New deterministic control approaches for crossover and mutation rates are defined, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic decreasing of High Crossover (ILM/DHC).
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CIXL2: a crossover operator for evolutionary algorithms based on population features
TL;DR: A crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions that takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring.
Patent
Management training simulation method and system
TL;DR: A management training simulation system and method is implemented on a computer and represents changes in design opportunities of objects in a simulated environment such as, for example, new or changed features in a product made by a particular firm as mentioned in this paper.
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Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization
TL;DR: Numerical results show that DYCORS algorithms are generally better than EGO, DDS, LMSRBF, MADS with kriging, SQP, an RBF-assisted evolution strategy, and a genetic algorithm and are a promising approach for watershed calibration and for HEB optimization.
Journal ArticleDOI
An effective krill herd algorithm with migration operator in biogeography-based optimization
TL;DR: A biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks, and it is shown that this novel BBKH approach performs better than the basic KH and other optimization algorithms.