Analog circuit topology synthesis by means of evolutionary computation

Rojec, Žiga; Bűrmen, Árpád; Fajfar, Iztok

doi:10.1016/j.engappai.2019.01.012

Cited by 36 publications

(22 citation statements)

References 28 publications

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“…This is done using several manipulation techniques. Using experience and advice from other experiments [17,5,18] we take the best individuals from the current generation and move them into the next until we fill one tenth of it (so in our case of 300 individuals per generation we allow the best 30 individuals to proceed into the next one). Since we already evaluated these individuals, we will not need to do so again.…”

Section: Population Manipulationmentioning

confidence: 99%

“…We therefore designed a different (multi criteria) fitness function as suggested by Rojec et al [5]. The proposed function allows focus on several characteristics at the same time and also allows assigning different priorities to each of them.…”

Section: Fitness Functionsmentioning

confidence: 99%

“…At the beginning, we retained the RMSE fitness function (and had to generate a comparison circuit for each example) but we quickly discovered that this fitness function did not seem to have a very high success rate -while we were always able to create a circuit with filter-like characteristics (i.e., with a cut-off frequency and dampening) we were unable to do so in a consistent manner. Upon reflection (see 2.2 for more detail) we switched to a multi-criteria fitness function as suggested by Rojec et al [5].…”

Section: Second-order Filtersmentioning

confidence: 99%

“…The rise of easily accessible (and more powerful) computers led to the research and development of tools that are capable (to some degree) of creating the desired topology automatically given a list of possible elements (resistors, coils, generators etc.) [5,6] and circuit characteristics. Such tools are of great interest since they streamline the design process, save a lot of time and also grant the possibility of circuit design to a user who might not be well-versed in physical circuit design.…”

Section: Introductionmentioning

confidence: 99%

“…It was already successfully used for simple circuit generation by Castejon et al [6] but lacked a more complex fitness function. In this paper, we propose a combination of the GE approach together with a complex fitness function, similar to the one proposed by Rojec et al [5]. The use of such function allows fine-tuning of several parameters at the same time (slope, gain, cut-off frequency etc.)…”

Section: Introductionmentioning

confidence: 99%

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Grammatical Evolution-based Analog Circuit Synthesis

Kunaver

2020

Informacije MIDEM

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Computer aided circuit design is becoming one of the mainstream methods for helping circuit designers. Multiple new methods have been developed in this field including Evolutionary Electronics. A lot of work has been done in this field but there is still a room for improvement since some of the solutions lack the flexibility (diversity of components, limited topology etc.) in circuit design or lack complex fitness functions that would enable the synthesis of more complex circuits. The research presented in this article aims to improve this by introducing Grammatical Evolution-based approach for circuit synthesis. Grammatical Evolution offers great flexibility since it is rule based-adding a new element is as simple as writing one additional line of initialization code. In addition, the use of a complex multi-criteria function allows us to create circuits that can be as complex as required thus further increasing the flexibility of the approach. To achieve this, we use a combination of Python and SPICE to create a series of netlists, evaluate them in the PyOpus environment, and select the best possible circuit for the task. We demonstrate the efficiency of our approach in three different case studies where we automatically generate oscillators and high/low-pass filters of second and third order.

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Section: Population Manipulationmentioning

confidence: 99%

Section: Fitness Functionsmentioning

confidence: 99%

Section: Second-order Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Grammatical Evolution-based Analog Circuit Synthesis

Kunaver

2020

Informacije MIDEM

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Active-learning-based reconstruction of circuit model

2021

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A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit

Shi

Minku

Yao

2022

Genet Program Evolvable Mach

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When applying evolutionary algorithms to circuit design automation, circuit representation is the first consideration. There have been several studies applying different circuit representations. However, they still have some problems, such as lack of design ability, which means the diversity of evolved circuits was limited by the circuit representation, and inefficient transformation from circuit representation into SPICE (Simulation Program with Integrated Circuit Emphasis) netlist. In this paper, a novel tree-based circuit representation for analog circuits is proposed, which is equipped with an intuitive and three-terminal devices friendly mapping rule between circuit representation and SPICE netlist, as well as a suitable crossover operator. Based on the proposed representation, a framework for automated analog circuit design using genetic programming is proposed to evolve both the circuit topology and device values. Three benchmark circuits are applied to evaluate the proposed approach, showing that the proposed method is feasible and evolves analog circuits with better fitness and number of components while using less fitness evaluations than existing approaches. Furthermore, considering physical scalability limits of conventional circuit elements and the increased interest in emerging technologies, a memristor-based pulse generation circuit is also evolved based on the proposed method. The feasibility of the evolved circuits is verified by circuit simulation successfully. The experiment results show that the evolved memristive circuit is more compact and has better energy efficiency compared with existing manually-designed circuits.

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Analog circuit topology synthesis by means of evolutionary computation

Cited by 36 publications

References 28 publications

Grammatical Evolution-based Analog Circuit Synthesis

Grammatical Evolution-based Analog Circuit Synthesis

Active-learning-based reconstruction of circuit model

A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit

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