Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Towards the Automatic Design of More Efficient Digital Circuits
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Promises and challenges of evolvable hardware
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
CBR-Based knowledge discovery on results of evolutionary design of logic circuits
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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To improve evolutionary design of circuits in efficiency, scalability and optimizing capability, a genetic algorithm based approach was proposed. It employs a gate-level encoding scheme supporting flexible changes of functions and interconnections of comprised logic cells, a multi-objective evaluation mechanism of fitness with weight-vector adaptation and circuit simulation, and an adaptation strategy for crossover probability and mutation probability to vary with individuals’ diversity and genetic-search process. It was validated by experiments on arithmetic circuits, obtaining circuits with expected functions, novel structures, and higher performances in gate usage and operating speed as compared with the results of both conventional and evolutionary approaches. Moreover, by studying the circuits evolved for problems of increasing scales, some novel, efficient and generalized principles have been discerned, which are easy to verify but difficult to dig out by human experts with existing knowledge.