Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
A Scalable Approach to Evolvable Hardware
Genetic Programming and Evolvable Machines
Two-Step Incremental Evolution of a Prosthetic Hand Controller Based on Digital Logic Gates
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
A Pattern Recognition System Using Evolvable Hardware
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Hardware Evolution at Function Level
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An Extrinsic Function-Level Evolvable Hardware Approach
Proceedings of the European Conference on Genetic Programming
A Divide-and-Conquer Approach to Evolvable Hardware
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
EH '99 Proceedings of the 1st NASA/DOD workshop on Evolvable Hardware
Bidirectional Incremental Evolution in Extrinsic Evolvable Hardware
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A hardware design of neuromolecular network with enhanced evolvability: a bioinspired approach
Journal of Electrical and Computer Engineering - Special issue on Networks-on-Chip: Architectures, Design Methodologies, and Case Studies
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Bidirectional incremental evolution (BIE) has been proposed as a technique to overcome the "stalling" effect in evolvable hardware applications. However preliminary results show perceptible dependence of performance of BIE and quality of evaluated circuit on assembling strategy applied during reverse stage of incremental evolution. The purpose of this paper is to develop assembling strategy that will assist BIE to produce relatively optimal solution with minimal computational effort (e.g. the minimal number of generations).