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 programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Principles in the Evolutionary Design of Digital Circuits—Part II
Genetic Programming and Evolvable Machines
Promises and Challenges of Evolvable Hardware
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Synthesis of Synchronous Sequential Logic Circuits from Partial Input/Output Sequences
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Proceedings of the European Conference on Genetic Programming
Bidirectional Incremental Evolution in Extrinsic Evolvable Hardware
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Evolvable Components: From Theory to Hardware Implementations
Evolvable Components: From Theory to Hardware Implementations
Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits
Genetic Programming and Evolvable Machines
Investigating the performance of module acquisition in cartesian genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolution of Asynchronous Sequential Circuits
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Development Brings Scalability to Hardware Evolution
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Fundamentals of Logic Design
Design of sequential circuits by quantum-dot cellular automata
Microelectronics Journal
Dynamical Evolution in Function Finding
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
The Input Pattern Order Problem: Evolution of Combinatorial and Sequential Circuits in Hardware
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Practical and scalable evolution of digital circuits
Applied Soft Computing
A developmental method for growing graphs and circuits
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Evolution of self-diagnosing hardware
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Genetic Programming and Evolvable Machines
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
In this study, we propose a module-level three-stage approach (TSA) to optimize the evolutionary design for synchronous sequential circuits. TSA has a three stages process, involving a genetic algorithm (GA), a pre-evolution, and a re-evolution. In the first stage, the GA simplifies the number of states and automatically searches the state assignment that can produce the circuit with small complexity. Then, the second stage evolves a set of high-performing circuits to acquire frequently evolved blocks, which will be re-used for more compact and simple solutions in the next stage. In this stage, a genetic programming (GP) is proposed for evolving the high-performing circuits and data mining is used as a finder of frequently evolved blocks in these circuits. In the final stage, the acquired blocks are encapsulated into the function and terminal set to produce a new population in the re-evolution. The blocks are expected to make the convergence faster and hence efficiently reduce the complexity of the evolved circuits. Seven problems of three types--sequence detectors, modulo-n counters and ISCAS89 circuits--are used to test our three-stage approach. The simulation results for these experiments are promising, and our approach is shown to be better than the other methods for sequential logic circuits design in terms of convergence time, success rate, and maximum fitness improvement across generations.