An overview of evolutionary algorithms: practical issues and common pitfalls

By L. D. Whitley. Information and Software Technology, 2001

483 citations recorded by the source corpus.

Abstract An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/7417f4bae3d12366afe3194649c1f5a20654a635

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

Citations in the corpus

Adaptive models for large herbivore movements in heterogeneous landscapes (2005, methodology): “…set of desired ‘‘training’’ outputs, but since we were interested in the cumulative efficiency of behavioral decisions rather than on the immediate outputs of the ANNs, we use a genetic algorithm (Goldberg 1989; Whitley 2001) to search for connection weights that might provide efficient behaviors.”
An Evolutionary Game Theoretic Analysis of Difference Evaluation Functions (2015, background): “EAs typically contain two basic mechanisms: mutation and selection [12].”
Search Based Software Engineering: Techniques, Taxonomy, Tutorial (2010, background): “Whitley’s tutorial papers [101, 102] offer a further excellent introductory material for getting starting with Genetic Algorithms in Search Based Software Engineering.”
Evolutionary artificial neural networks: a review (2011, methodology): “Since the 1990s, Evolution Algorithms(EAs) have been successfully used for optimizing the design and the parameters of ANNs (Whitley 2001).”
Evolutionary algorithms: A critical review and its future prospects (2016, background): “Evolutionary computing involves various algorithms, commonly known as Evolutionary Algorithms (EAs) [2][3].”
Search‐based software test data generation: a survey (2004, background): “For shorter overviews and tutorials, see references [19] [9] [20] .”
Novel meta-heuristic bald eagle search optimisation algorithm (2019, background): “Individuals must adapt to their surrounding environments to ensure the survival and long-term preservation of their breed (Whitley 2001).”
Search-Based Software Testing: Past, Present and Future (2011, methodology): “For an excellent introduction on getting starting with Genetic Algorithms in Search-Based Software Engineering, see Whitley’s tutorial papers [23], [24].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “More detailed discussion of EC strengths, issues, and theoretical foundations and aspects such as computational complexity and algorithm convergence may be found in [13,101,110,173,380,399].”
Variable selection in regression—a tutorial (2010, methodology): “Other often used methods based on the evolution theory and resembling GA are genetic programming, evolution strategies and evolutionary algorithms [8,9].”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “However with the increase of processing power as well as the possibility to easily parallelize evolutionary methods, especially in combination with cloud services and the island model [105], the issue should be, at most, of a temporary nature.”