Simulated annealing: theory and applications
Simulated annealing: theory and applications
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
The genetic algorithm as a discovery engine: strange circuits and new principles
Creative evolutionary systems
Logic Synthesis and Optimization
Logic Synthesis and Optimization
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
Principles in the Evolutionary Design of Digital Circuits—Part II
Genetic Programming and Evolvable Machines
A chart method for simplifying truth functions
ACM '52 Proceedings of the 1952 ACM national meeting (Pittsburgh)
Evolutionary Multiobjective Design of Combinational Logic Circuits
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Design of combinational logic circuits through an evolutionary multiobjective optimization approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
Use of particle swarm optimization to design combinational logic circuits
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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In this article, we perform a comparative study of different heuristics used to design combinational logic circuits. This study mainly emphasizes the use of local search hybridized with a genetic algorithm (GA) and the impact of introducing parallelism. Our results indicate that a hybridization of a GA with a local search algorithm (simulated annealing) is beneficial and that the use of parallelism not only introduces a speedup in the algorithms compared (as expected) but also allows us to improve the quality of the solutions found.