Proceedings of the Genetic and Evolutionary Computation Conference

By William B. Langdon, Erick Cantú-Paz, Keith E. Mathias, Rajkumar Roy, David Davis, Riccardo Poli, K. Balakrishnan, Vasant G Honavar, G. Rudolph, J. Wegener and 6 more. 2002

78 citations recorded by the source corpus.

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…(Kitano, 1990), Cellular Encoding (Gruau, Whitley, & Pyeatt, 1996), HyperNEAT (Clune, Stanley, Pennock, & Ofria, 2011; D’Ambrosio & Stanley, 2007; Stanley, D’Ambrosio, & Gauci, 2009; van den Berg & Whiteson, 2013) (extending NEAT; Section 6.6), and extensions thereof (e.g., Risi & Stanley, 2012).”
Fundamentals of natural computing: an overview (2007, unknown): “Recent research into the particle swarm optimization algorithm has emphasized its application to multi-objective, dynamic, constrained and combinatorial optimization problems [33,52,214,287]; theoretical investigations, such as scalability issues, novel operators and convergence analysis [41, 136,28”
Autonomous Evolution of Topographic Regularities in Artificial Neural Networks (2010, methodology): “Like other work on NE (Gomez, Schmidhuber, & Miikkulainen, 2008; Stanley & Miikkulainen, 2002c; Whiteson, Kohl, Miikkulainen, & Stone, 2003), it includes a performance comparison that demonstrates the advantage of its approach—in this case, in the domain of checkers.”
Ant colony optimization: Introduction and recent trends (2005, background): “Maximum clique problem Bui and Rizzo Jr [20]”
Feature Construction Methods : A Survey (2009, methodology): “[Otero et al., 2002] used genetic programming to construct features for”
Understanding knee points in bicriteria problems and their implications as preferred solution principles (2011, background): “‘Innovization’ within the knee region Innovization is a technique to unveil important solution principles common to a set of Paretooptimal solutions (Deb and Srinivasan 2006).”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, unknown): “The constricted PSO still compares favourably, especially in problems with lower dimensionality—a prime example of the ''No Free Lunch'' theorems of Wolpert and Macready (1997).”
A review of particle swarm optimization. Part I: background and development (2007, methodology): “In a move away from the biological metaphor, Blackwell and Bentley (2002) utilised an analogy of electrostatic energy to track a dynamic global optimum.”
Semantically-based crossover in genetic programming: application to real-valued symbolic regression (2011, unknown): “It is generally believed that using a representation with high locality (small change in genotype correspond to small change in phenotype) is important for efficient evolutionary search [17, 21, 52, 15, 16].”