Elements of information theory
Elements of information theory
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 algorithms as a computational tool for design
Genetic algorithms as a computational tool for design
On the generation of multiplexer circuits for pass transistor logic
DATE '00 Proceedings of the conference on Design, automation and test in Europe
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 Scalable Approach to Evolvable Hardware
Genetic Programming and Evolvable Machines
GA-Based Learning of kDNFns Boolean Formulas
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Learning Heuristics for OBDD Minimization by Evolutionary Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Genetic Programming Approach to Logic Function Synthesis by Means of Multiplexers
EH '99 Proceedings of the 1st NASA/DOD workshop on Evolvable Hardware
Scalability Problems of Digital Circuit Evolution: Evolvability and Efficient Designs
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Bidirectional Incremental Evolution in Extrinsic Evolvable Hardware
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Evolutionary Multi-Level Network Synthesis in Given Design Style
ISMVL '00 Proceedings of the 30th IEEE International Symposium on Multiple-Valued Logic
Information Theory Method for Flexible Network Synthesis
ISMVL '01 Proceedings of the 31st IEEE International Symposium on Multiple-Valued Logic
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Explorations in design space: unconventional electronics designthrough artificial evolution
IEEE Transactions on Evolutionary Computation
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In this paper, we propose the use of Information Theory as the basis for designing a fitness function for Boolean circuit design using Genetic Programming. Boolean functions are implemented by replicating binary multiplexers. Entropy-based measures, such as Mutual Information and Normalized Mutual Information are investigated as tools for similarity measures between the target and evolving circuit. Three fitness functions are built over a primitive one. We show that the landscape of Normalized Mutual Information is more amenable for being used as a fitness function than simple Mutual Information. The evolutionary synthesized circuits are compared to the known optimum size. A discussion of the potential of the Information-Theoretical approach is given.