Introduction to Evolutionary Computing
By Robert A. Ghanea-Hercock. 2003
1,307 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/7e80fa05ce2d429862bdc31c7460aa4b71e8a559
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Pinar Çivicioglu
- Seyedali Mirjalili
- Salvador Garcia
- Alberto Fernández
- Janez Brest
In Computational Theory and Mathematics
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Pinar Çivicioglu
- Seyedali Mirjalili
- Janez Brest
- Xin-She Yang
- Efrén Mezura-Montes
- Seyed Mohammad Mirjalili
Citations in the corpus
- Parameter tuning for configuring and analyzing evolutionary algorithms (2011, background): “Evolutionary algorithms form a class of heuristic search methods based on a particular algorithmic framework whose main components are the variation operators (mutation and recombination) and the selection operators (parent selection and survivor selection), cf. [3].”
- KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, background): “Evolutionary Algorithms (EAs) (Eiben and Smith 2003) are optimization algorithms based on natural evolution and genetic processes.”
- Imitative and Direct Learning as Interacting Factors in Life History Evolution (2017, neutral): “A standard tournament selection approach [27] is sufficient to model all such deaths, with an appropriate number of random pairs of individuals competing each simulated year by having their performance compared, and only the winners surviving.”
- Fuzzy Logic as Agents' Knowledge Representation in A-Trader System (2015, neutral): “The EvolutionaryFuzzy agent is created on the basis of an evolutionary algorithm [46, 47].”
- Genetic process mining: an experimental evaluation (2007, methodology): “To overcome the limitations of the current process mining techniques, our research uses genetic algorithms (Eiben and Smith 2003) to mine process models.”
- Genetic Process Mining (2005, background): “Genetic algorithms are adaptive search methods that try to mimic the process of evolution [9].”
- Kriging is well-suited to parallelize optimization (2010, neutral): “A good example is provided by the evolutionary algorithms [9] which have progressively mixed the genetic metaphor and stochastic optimization theory.”
- Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, background): “Later, it was realized that in order to achieve optimal convergence, these parameters should be altered in the evolution process itself [5], [ 7 ].”
- Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, background): “, GAs, EE, and EP [1]), how to decide which algorithm to use for a particular application has become an important issue.”
- Pareto Multi Objective Optimization (2005, neutral): “Evolutionary techniques have been successfully applied to all sort of SO optimization, especially those where the objective functions is not well-behaved (not differentiable, discontinuous, and/or no analytical formulation) [ [3], [12]- [14]].”
- A comparative study of Artificial Bee Colony algorithm (2009, background): “Two important classes of population-based optimization algorithms are evolutionary algorithms [1] and swarm intelligence-based algorithms [2].”