Multi-objective optimization of QCA circuits with multiple outputs using genetic programming

Rezaee, Razieh; Houshmand, Mahboobeh; Houshmand, Monireh

doi:10.1007/s10710-012-9173-6

Cited by 11 publications

(9 citation statements)

References 22 publications

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“…The internal nodes can be the majority (M) and inverter (I) gates, while the external nodes (leaves) are variables or the logic '1'. This chromosome has been altered later [21,22], to implement a two-output circuit. In most cases, the aim is to reduce the number of majority gates.…”

Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Optimization of Quantum Cellular Automata Circuits by Genetic Algorithm

Parvane

Rahimi

Jafarinejad

2020

IJE

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Quantum cellular automata (QCA) enables performing arithmetic and logic operations at the molecular scale. This nanotechnology promises high device density, low power consumption and high computational power. Unlike the CMOS technology where the ON and OFF states of the transistors represent binary information, in QCA, data is represented by the charge configuration. The primary and basic device in this paradigm is the three-input majority gate, thus in QCA, the conventional AND-OR mapping for implementation of logic functions is not effective. We introduce four primitive admissible geometric patterns, which aid in the identification of majority functions. For a nonmajority function, a genetic algorithm (GA) is used to map the function to at most four majority gates in a wide range of implementations. We show that the emergence of specific genes will result in a further reduction in the number of majority gates in the network. The GA is intrinsically parallel and results in variety of implementations, which allows merging the layout and logic levels of the design and provides an important approach towards designing high-performance QCA circuits.

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Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Optimization of Quantum Cellular Automata Circuits by Genetic Algorithm

Parvane

Rahimi

Jafarinejad

2020

IJE

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“…The diversity in the population for grammar-driven MOGP and symbolic regression was studied by Tsakonas (Tsakonas, 2014). Rezaee (Rezaee et al, 2013) minimized two different objectives in the optimization of Quantum-Dot Cellular Automata electronic circuits. Finally, Olague performed an automatic synthesis of operators for image analysis (Olague and Trujillo, 2012), employing a threeobjective approach.…”

Section: Evolution Control: Dominance and Elitismmentioning

confidence: 99%

Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming

Echevarría

Blanco

Sánchez

2019

Engineering Applications of Artificial Intelligence

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“…We express our sincere gratitude to the authors of [8, 9] for providing us with their results of genetic algorithm, for 3‐variable and 4‐variable logic functions.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Nonetheless, this individual specification could reduce the circuit's reliability. In many studies, delay, area and reduction of gate elements are discussed [6][7][8][9][10][11], yet the reliability is one of the most significant parameters which must be investigated in every technology.…”

Section: Introductionmentioning

confidence: 99%

Improving logic function synthesis, through wire crossing reduction in quantum‐dot cellular automata layout

Taherifard

2015

IET Circuits, Devices & Systems

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Quantum-dot cellular automata, as the successor of metal-oxide semiconductor field-effect transistors, are one of the promising nanotechnology devices, which have attracted myriad researchers in the recent decade. In this technology, coplanar wire crossing is one of the unique specifications that can reduce its reliability. In the present study, a heuristic method is introduced using the Karnaugh-Map (K-Map) to minimise the number of wire crossing as the first step. Afterwards, it attempts to replace each wire crossing with three non-wire crossing XOR gates that reduce all wire crossings to zero as the second step. Experimental results reveal that, reducing wire crossings to zero, the authors method for 3-variable functions lowers the number of gates about 54, 42, 58 and 59%, respectively, in comparison with K-Map, Genetic, Gate-Optimise and Universal Quantum-dot Cellular Automata Logic Gate (UQCALG) methods. For 4-variable functions, their method decreases the number of gates almost 64 and 58%, respectively.

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Multi-objective optimization of QCA circuits with multiple outputs using genetic programming

Cited by 11 publications

References 22 publications

Optimization of Quantum Cellular Automata Circuits by Genetic Algorithm

Optimization of Quantum Cellular Automata Circuits by Genetic Algorithm

Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming

Improving logic function synthesis, through wire crossing reduction in quantum‐dot cellular automata layout

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