Adaptive immune genetic algorithm for logic circuit design

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.