Rendell, L. R. 1985. Genetic plans and the probabilistic learning system: Synthesis and results. In Proceedings of an international conference on genetic algorithms and their applications, 6073. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Financial Forecasting Using Genetic Algorithms - Mahfoud, Mani (1996) (6 citations) (Correct)
....using GAs. These structures have ranged from neural network weights and topologies (Gruau Whitley, 1993; Whitley et al. 1990, 1991, 1993; Whitley Schaffer, 1992) to LISP programs (Koza, 1992) to regions of the instance space similar to decision trees induced by a splitting algorithm (Rendell, 1983, 1985; Sikora Shaw, 1994) to expertsystem rules (Montana, 1990) to weights for a game s evaluation function (Rendell, 1990) to weights and orientations for the k nearest neighbor algorithm (Kelly Davis, 1991; Punch et al. 1993) to finite state automata (Fogel et al. 1966) and context free ....
Rendell, L. R. 1985. Genetic plans and the probabilistic learning system: Synthesis and results. In Proceedings of an international conference on genetic algorithms and their applications, 6073.
Using Genetic Algorithms for Concept Learning - De Jong, Spears, Gordon (1993) (49 citations) (Correct)
....of this approach is that, if an effective mapping can be defined, a standard off the shelf GA can be used with few, if any, changes. In this paper, we illustrate the latter approach and develop a system which uses a traditional GA with minimal changes. For examples of the other approach see (Rendell, 1985; Grefenstette, 1989; Koza, 1991; Janikow, 1991) The decision to adopt a minimalist approach has immediate implications for the choice of concept description languages. We need to identify a language that can be 1 Excellent introductions to GAs can be found in (Holland, 1975) ....
Rendell, L. (1985). Genetic plans and the probabilistic learning system: Synthesis and results. Proceedings of the First International Conference on Genetic Algorithms (pp. 60 - 73). Pittsburgh, PA: Lawrence Erlbaum.
Performance Comparison of a Similarity-Based Learner, a Genetic.. - Liao (Correct)
....algorithm optimized for local search to a GA, thus improving the GA s local search capability. This study takes such an approach by introducing a new operator to perform local search. There exist a few demonstrations of the efficacy of GA SBL hybrid algorithms in the literature. For example, PLS2 [8] employs a genetic algorithm which manipulates populations of regions derived by PLS1, an SBL algorithm. PLS2 can learn an evaluation function for game tree search in the fifteen puzzle and produce a function superior in accuracy to one derived by SBL and roughly equal to one from a standalone GA ....
Rendell, L.A. "Genetic Plans and the Probabilistic Learning System: Synthesis and Results," in Proceedings of the Fifth International Conference on Genetic Algorithms, 1985.
Adaptive Strategy Selection for Concept Learning - Spears, Gordon (1991) (1 citation) (Correct)
....syntactic and semantic constraints, and that can be of widely varying length and complexity. There are two general approaches one might take to resolve this issue. The first involves changing the fundamental GA operators (crossover and mutation) to work effectively with complex non string objects (Rendell, 1985). This must be done carefully in order to preserve the properties that make the GAs effective adaptive search procedures (see (DeJong, 1987) for a more detailed discussion) Alternatively, one can attempt to construct a string representation that minimizes any changes to the GAs. In this paper, ....
Rendell, L., Genetic Plans and the Probabilistic Learning System: Synthesis and Results. Proc. 1st Int'l Conference on Genetic Algorithms and their Applications. 1985.
Learning Concept Classification Rules Using Genetic Algorithms - De Jong, Spears (1991) (13 citations) (Correct)
....syntactic and semantic constraints, and which can be of widely varying length and complexity. There are two general approaches one might take to resolve this issue. The first involves changing the fundamental GA operators (crossover and mutation) to work effectively with complex non string objects [Rendell, 1985]. This must be done carefully in order to preserve the properties which make the GAs effective adaptive search procedures (see [DeJong, 1987] for a more detailed discussion) Alternatively, one can attempt to construct a string representation which minimizes any changes to the GAs. We are ....
Rendell, L. (1985). Genetic Plans and the Probabilistic Learning System: Synthesis and Results. Proc. 1st Int'l Conference on Genetic Algorithms and their Applications.
PAGODA: A Model for Autonomous Learning in Probabilistic Domains - desJardins (1992) (Correct)
No context found.
Larry Rendell. Genetic plans and the Probabilistic Learning System: Synthesis and results. Technical Report UIUCDCS-R-85-1217, University of Illinois at Urbana-Champaign, 1985.
Using Genetic Algorithms for Supervised Concept Learning - Spears, De Jong (6 citations) (Correct)
No context found.
Rendell, L. (1985). Genetic Plans and the Probabilistic Learning System: Synthesis and Results. Proc. 1st Int'l Conference on Genetic Algorithms and their Applications.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC