Paper Info
Title
Evolvability limits: a case study concerning the modular evolvable capacities (MECs) of a new neural net model for a second generation brain building machine BM2
Abstract
This paper concerns a case study of the limits to which desirable properties can be evolved in a particular kind of neural network circuit (module). It has been the decade long dream of the author to evolve neural network modules in their 10,000s at electronic speeds in special evolvable hardware, and then to assemble them into a gigabyte of memory to build artificial brains. But such an dream is only realizable if the (modular) evolvable capacities (MECs) of such modules (i.e. a qualitative and quantitive measure of the quality of the evolution) are sufficiently high to make the effort worthwhile. This paper shows how the evolvable capacities of a module using a new neural network model (called DePo) was stretched to its limits. This paper makes the claim that evolutionary engineering is all about "pushing up MECs"
Year
DOI
Venue
2002
10.1109/CEC.2002.1006277
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress  
Keywords
DocType
Volume
brain models,evolutionary computation,neural chips,bm2 second generation brain building machine,artificial brains,evolutionary engineering,evolvability limits,modular evolvable capacities,neural net model,neural network circuit,neural network modules,field programmable gate arrays,hardware,computer science,neural network,artificial neural networks,circuits,neural network model,neural net
Conference
1
Citations 
PageRank 
References 
0
0.34
1
Authors
1
Name
Order
Citations
PageRank
de Garis, H.1124.33