Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
EH '99 Proceedings of the 1st NASA/DOD workshop on Evolvable Hardware
Evolutionary Multiobjective Design of Combinational Logic Circuits
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Evolvable Hardware (Genetic and Evolutionary Computation)
Evolvable Hardware (Genetic and Evolutionary Computation)
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In this paper, we study the design of combinational logic circuits using evolutionary algorithms. In particular, this paper is about fitness assignment methods and recombination operators for speeding up the optimisation process. We propose a new fitness assignment mechanism called MaxMin method and compare it with the straightforward method used in the literature. The results show significant improvements both in terms of computational time and quality of the solutions. Furthermore, a new cross-over operator called area cross-over has been introduced and compared with other typical operators. This operator is particularly designed for gate matrices where two rectangular logic blocks are exchanged between the individuals. We observe that the MaxMin fitness assignment as well as the area cross-over operator considerably improve the performance of the evolutionary optimisation.