Two-level logic minimization: an overview
Integration, the VLSI Journal
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Logic Design of Digital Systems
Logic Design of Digital Systems
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Journal of Systems Architecture: the EUROMICRO Journal
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
VLSID '98 Proceedings of the Eleventh International Conference on VLSI Design: VLSI for Signal Processing
Logic synthesis for vlsi design
Logic synthesis for vlsi design
Advanced AI Search Techniques in Modern Digital Circuit Synthesis
Artificial Intelligence Review
Advanced AI search techniques in modern digital circuit synthesis
Artificial intelligence in logic design
Quality-driven model-based architecture synthesis for real-time embedded SoCs
Journal of Systems Architecture: the EUROMICRO Journal
Cooperation in the context of sustainable search
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary sets of safe ship trajectories: problem-dedicated operators
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Application of Genetic Algorithm in unit selection for Malay speech synthesis system
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Genetic algorithms (GA) have several important features that predestine them to solve design problems. Their main disadvantage however is the excessively long run-time that is needed to deliver satisfactory results for large instances of complex design problems. The main aims of this paper are (1) to demonstrate that the effective and efficient application of the GA concept to design problem solving requires substitution of the basic GAs natural evolution by genetic engineering (GE), (2) to propose and discuss the concept of a genetic engineering algorithm (GEA), and (3) to show how to apply the GEA to solve synthesis problems. In this paper, an effective and efficient GE scheme is proposed and applied to solve an important design problem: the minimal input support problem. In almost all cases, our GEA produces strictly optimal results and realizes a very good trade-off between effectiveness and efficiency. The experimental results clearly demonstrate that the proposed GE scheme is suitable for solving design problems and its application results in very effective and efficient GEAs.