Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Principles in the Evolutionary Design of Digital Circuits—Part II
Genetic Programming and Evolvable Machines
A comparison of two circuit representations for evolutionary digital circuit design
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
An Efficient Multi-Objective Evolutionary Algorithm for Combinational Circuit Design
AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
Immune-based evolutionary algorithm for fabric evaluation
Mathematics and Computers in Simulation
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
Genetic Algorithm for Boolean minimization in an FPGA cluster
The Journal of Supercomputing
An immune genetic algorithm with orthogonal initialization for analog circuit design
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Parallel algorithm for evolvable-based boolean synthesis on GPUs
Analog Integrated Circuits and Signal Processing
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Evolutionary design of circuits (EDC), an important branch of evolvable hardware which emphasizes circuit design, is a promising way to realize automated design of electronic circuits. In order to improve the evolutionary design of logic circuits in a more efficient, scalable and capable way, an Adaptive Immune Genetic Algorithm (AIGA) was designed. The AIGA draws into the mechanisms in biological immune systems such as clonal selection, hypermutation, and immune memory. Besides, the AIGA features an adaptation strategy that enables crossover probability and mutation probability to vary with genetic-search process. Our results are compared with those produced by the Multi-objective Evolutionary Algorithm (MOEA) and the Simple Immune Algorithm (SIA). The simulation results show that AIGA overcomes the disadvantages of premature convergence, and improves the global searching efficiency and capability.