Cognitive systems based on adaptive algorithms

By John H. Holland, J. Reitman. 1977

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “This idea led to the belief that intelligent computer programs could emerge with little human intervention by means of evolution in-silico (Holland and Reitman, 1977; Koza, 1992; Fogel, 2006).”
Global Optimization Algorithms -- Theory and Application (2009, methodology): “Besides the specialized Algorithmic Chemistry approach of Lasarczyk and Banzhaf [878; 879], there exists one very general class of evolutionary algorithms that elegantly circumvents positional epistasis(46): the Learning Classifier Systems (LCS) family [881, 882] which you can find discussed in Chap”
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal (2012, background): “Finally, John Holland in his development of Learning Classifier Systems had in mind a cognitive implementation, but had proposed no model of such an implementation (Holland, 1975; Holland and Reitman, 1977; Holland et al., 1986) although recent work in learning classifier systems has done so (Fernan”
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents (2000, background): “One of the earliest extensions to the basic evolutionary model for the support of coadapted subcomponents is the classifier system (Holland and Reitman, 1978; Holland, 1986).”
Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, methodology): “Some popular algorithms in this field are Genetic Algorithms (GA) [1, 2], Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4], Evolutionary Strategy (ES) [5], Differential Evolution (DE) [6], and Evolutionary Programming (EP) [7].”
Robust non-linear control through neuroevolution (2003, background): “Early work in this area was done by Holland and Reitman (1978) in Classifier Systems.”
Hierarchical Chunking in Classifier Systems (1994, background): “There are many variants of the BBA -e.g., see (Dorigo, 1991; Huang, 1989; Riolo, 1990; Wei& 1991; Wilson, 1985, 1987) -as well as of the PSP -e.g., see (Grefenstette, 1988; Holland & Reitman, 1978; Wei& 1992).”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “These kinds of systems are usually called learning classiﬁer systems [58].”
Ten years of genetic fuzzy systems: current framework and new trends (2001, methodology): “When considering a rule based system and focusing on learning rules, there are three main approaches that have been applied in the literature: Pittsburgh approach [61], Michigan approach [34] and Iterative rule learning approach [66].”
A Classification of Hyper-heuristic Approaches (2010, background): “Classifier system hyper-heuristic for bin packing: Classifier systems [34] are rule-based learning systems that evolve fixed length stimulus-response rules.”
Grasshopper optimization algorithm for multi-objective optimization problems (2017, background): “To resolve this issue, the distance between grasshoppers should be mapped or normalized to [1, 4].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “1 General Framework From some of the early work on classifier systems two terms have emerged that are still in common usage today: Michigan-style [134] and Pittsburgh-style [348], the names being in recognition of the institutions of the originators of the two approaches.”
A novel genetic algorithm based on immunity (2000, background): “Holland led to GA as it is known today [2].”
Hierarchical Chunking in Classifier Systems (2008, background): “There are many variants of the BBA – e.g., see (Dorigo, 1991; Huang, 1989; Riolo, 1990; Weiß, 1991; Wilson, 1985, 1987) – as well as of the PSP – e.g., see (Grefenstette, 1988; Holland & Reitman, 1978; Weiß, 1992).”