H. de Garis, "Genetic Programming : Modular Neural Evolution for Darwin Machines", Proceedings IJCNN90 WASH DC, International Joint Conference on Neural Networks, January 1990, Washington DC. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Learning From Context Without Coding-Tricks - Case, Jain, Stephan (Correct)
....eventually converging to a program for f [6, 10] A class of functions S is Ex identifiable just in case there is a machine that Ex identifies each member of S. Here is a particularly simple example of a self referential coding trick. Let SD = f com 1 In other empirical work (for example, [42, 43, 41, 11, 12, 13, 16, 40, 39]) one pre trains on a succession of prior tasks to achieve success on a current task. Mastery of previous tasks provides useful context for the next. One sees similar attempts in animal training by shaping desired behavior through a succession of approximations [21, 15] e.g. to teach a dog to ....
H. de Garis. Genetic programming: Modular neural evolution for darwin machines. In M. Caudill, editor, IJCNN-90-WASH DC; International Joint Conference on Neural Networks, volume 1, pages 194--197. Lawrence Erlbaum Associates, Publishers, Hillsdale, New Jersey, January 1990.
Application of Genetic Algorithms to the Construction of .. - Schiffmann, Joost.. (1993) (11 citations) (Correct)
....It should be noted that GAs can be applied to neural networks in two different ways: 1. Optimizing connection weights 2. Optimizing network topology 3.1 GAs for weight adjustment In the first case the GA works at continuous parameters. Results concerned with this approach can be found in [8] [16]. An exciting discussion of evolutionary training methods is provided by [17] It can be summarized that the mentioned approaches differ mainly in three ways: 1. number representation 2. genetic operators used and 3. parent offspring replacement strategies If the number of connections is high it ....
de Garis H., Genetic Programming --- Modular Neural Evolution for Darwin Machines, Proc. of the Intern. Joint Conf. on Neural Networks, Vol I, pp. 194--197, 1990
The Design and Evolution of Modular Neural Network Architectures - Happel, Murre (1994) (33 citations) (Correct)
.... Miller, Todd and Hedge, 1989; Whitley and Bogart, 1990; Whitley and Hanson, 1989; Whitley and Starkweather, 1990) Others have taken a more direct approach, where not only the topology and learning parameters are specified by the genetic algorithm, but also all weight values and thresholds (e.g. De Garis 1990a, 1990b; Harp, Samad, and Guha, 1989) A much promissing and biologically more plausible method is the evolution of grammar based encodings of neural network structures (e.g. Boers, Kuiper, Happel, and Sprinkhuizen Kuyper, 1993; Gruau, 1991, 1992; Kitano, 1990; Merrill and Port, 1991) In many of ....
De Garis, H. (1990b). Genetic programming: modular neural evolution for Darwin machines. Proceedings of the International Joint Conference on Neural Networks, Washington DC, 1. Hillsdale, NJ: Lawrence Erlbaum, 194-197.
GENETIC PROGRAMMING - Building Artificial Nervous Systems with.. - de Garis (1990) (16 citations) Self-citation (De garis) (Correct)
No context found.
H. de Garis, "Genetic Programming : Modular Neural Evolution for Darwin Machines", Proceedings IJCNN90 WASH DC, International Joint Conference on Neural Networks, January 1990, Washington DC.
Brain Building - The Genetic Programming of Artificial Nervous.. - de Garis (1991) (3 citations) Self-citation (De garis) (Correct)
....: The LIZZY Project 9. Future Ideas for The LIZZY Project 10. Genetic Programming (GP) of Embryos 11. Future Embryo Ideas : Electronic Neuro Embryology 12. References 1. Introduction This article introduces the concept of Genetic Programming in a more general way than in earlier papers [2,3,4,5]. Genetic Programming (GP) is the application of the Genetic Algorithm [7,8] to the creation of systems which are too complex in their dynamics to be analyzed or pre specified in detail. Such systems can be built, but (probably) not understood. Two major applications of GP will be introduced in ....
....input neurons. However, what one does see clearly is the idea of hierarchical control, where control GenNets are used to command functional GenNets. This idea will be explored more extensively in the LIZZY experiment in section 8. The contents of this section are based on a recent paper of mine [2]. 0.96875 0.96875 0.9375 0.15625 FROM NEURON TO NEURON 0 1 2 3 0 1 2 3 0.84375 0.78125 0.71875 0.9375 0.875 0.875 0.75 0.3125 0.90625 0.78125 0.28125 0.65625 WEIGHTS FIG. 9 WEIGHT VALUES OF JOINT MODULE 7. Example 2 : Walker Having considered the time ....
[Article contains additional citation context not shown here]
H. de Garis, "Genetic Programming : Modular Neural Evolution for Darwin Machines", Proceedings IJCNN90 WASH DC, International Joint Conference on Neural Networks, January 1990, Washington DC.
Non-Redundant Genetic Coding of Neural Networks - Thierens (1996) (5 citations) (Correct)
No context found.
de Garis H. Genetic Programming: modular neural evolution for Darwin machines. Proceedings of International Joint Conference of Neural Networks. 1990.
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