Electronic Circuits Extrinsic Evolutionary Platform

Pedro Henrique Gouvea Coelho, J. F. M. do Amaral and M. C. Bentes

State Univ. of Rio de Janeiro, FEN/DETEL, R. S. Francisco Xavier, 524/Sala 5001E, Maracanã, R. J., 20550-900, Brazil

Keywords: Genetic Algorithms, Artificial Intelligence Applications, Evolutionary Electronics.

Abstract: This paper presents an electronic circuit evolution platform based on genetic algorithms with different

modes of operation. The platform has an extrinsic structure for evaluating individuals, making calls to a

circuit simulator for each possible solution evaluated. The platform can perform evolutions in search of

values for components, additional topologies to a fixed circuit and a search with total variation in the types

of components, values and connections. The assessed fitness can be based on a single objective, evaluating

only the output of the circuit, but also based on several objectives. The chosen method for this quantification

of multiple objectives is based on a Fuzzy System in order to facilitate the designer's specification. The

evolutions can be carried out in the time domain as well as in the frequency domain, being possible for the

user to change the operating mode without changes in the code already created. The exchange between the

operating modes, inputs used and the use of functions present on the platform is performed directly through

configuration variables, without the need to change the source code of the platform. In order to verify the

performance of the platform, each mode can be evaluated using different circuits with varying complexities.

Some selected case studies are shown in the paper to corroborate the feasibility of the method.

1 INTRODUCTION

With the advancement of technology in the area of

electronic engineering, the devices had their

processing capacity expanded and the space required

for implementation was reduced. This made it

possible for the equipment to become smaller and

smaller and with greater performance. Due to this

miniaturization capacity and high performance,

electronic devices have been used in a dedicated

way in the most diverse applications. Such devices,

known as Embedded Systems, allow to aggregate in

a single piece of equipment all the processing

necessary to perform a certain action. The additional

tools that a device can contain range from basic

diagnostic systems to additional tools to the

functions performed by the device. Whatever the

addition to the basic system, all aim to improve the

user experience by requiring minimal user

interference and integrating with other systems used.

The separation of systems into parts dedicated to a

specific task (modules) makes it possible to more

easily identify defective divisions and carry out

replacement more quickly. Such separation also

makes it possible to update the system by replacing

one module with another with superior performance.

However, despite facilitating the repair and updating

of systems, this segmentation does not seek to solve

any internal flaws. Such an approach considers the

use of devices until the appearance of failures and,

consequently, disposal of the defective module and

replacement with another with appropriate behavior.

Thus, this methodology directly affects the negative

technological impact on the environment,

contributing to the increase in electronic waste. In

addition to the concern with the environmental

impact of the accelerated disposal of devices, there

are applications that do not allow an easy

replacement of the modules. Applications in the

areas of space exploration and oil exploration

demand solutions to functional failures in a fast and

automatic way, due to the cost and complexity

associated with any required repair. For such

applications, a tool is sought that, associated with

the functioning of the devices, is able to circumvent

or mitigate the problems caused by any defective

part. This search comprises a specific line of study.

In order to enable adaptability to devices, a line of

research called Evolutionary Electronics (Haddow

and Tyrrell, 2018) can be used. This line of research

is based on the Evolutionary Computing technique,

which is inspired by Darwinian Evolution for

752

Coelho, P., M. do Amaral, J. and Bentes, M.

Electronic Circuits Extrinsic Evolutionary Platform.

DOI: 10.5220/0010480307520759

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 752-759

ISBN: 978-989-758-509-8

problem solving (Coello, 1999) (Coello, 2013)

(Labati et. al., 2016). The application of this

research, used in self-repair, seeks to evolve the

functioning of electronic circuits in an autonomous

way (Sinohara, 2001) (Santos et. al., 2012). This

paper is organized in four sections. The second

section describes the basics of evolutionary

environment and the proposed platform. Section

three discusses case studies in connection with the

evolutionary circuits platform. Finally section four

ends the paper with the conclusions.

2 EVOLUTIONARY STRUCTURE

2.1 Evolutionary Electronics

Along with the application of Computational

Evolution in the area of Electronics Engineering a

new line of research has emerged. This line of

research is called Evolutionary Electronics. In this

line of research, evolution is carried out through the

evaluation of electronic circuits and the objective is

to evolve such circuits until the desired specification

is obtained. (Greenwood and Tyrrel, 2007) (Reorda

et. al., 2017). To carry out the evolution of these

circuits, the same operators and the operating logic

used in Evolutionary Computing are used. However,

these characteristics must be adapted for this type of

evolution. When using Genetic Algorithms in the

evolution of circuits, the evaluated individuals are

the circuits instead of numerical solutions as in

Evolutionary Computation. The representation of

each individual is adapted to represent a specific

characteristic of circuits, a characteristic that will be

the target of the evolution process. The concepts of

population and generation are transported from one

line of research to the other without any change in

meaning. For both lines of research, the population

of individuals represents a set of possibilities or

representations for the target characteristic of

evolution. In the evolution of circuits, circuit

topologies evolve. This characteristic may vary

according to the designer, and may, for example,

represent the value of components or their tolerance.

Once the characteristics of a set of solutions are

determined, such individuals can be evaluated and

selected by the process responsible for assessing the

suitability of each possible solution or selection.

This selection and modification of the next

generation is then performed in an iterative way up

to the stopping criterion previously established for

the evolved circuit. This criterion can be used to

evaluate the response observed by the circuit or the

time required for the process, that is, a maximum

number of cycles or generations available for the

search process. The operators responsible for

modifying the population (set of solutions) will

exhibit the same behavior previously presented,

behavior responsible for the efficiency of the

method. The combination operator (Crossover) will

be responsible for merging the representations in

search of a better performing solution. The rate of

the population that will perform the combination

directly affects the speed of evolution, as it

determines the speed of variation observed in each

search cycle. The mutation process will be carried

out on the combined individuals, attributing a

random character on the evolved characteristic. This

allows evolution to discover promising

characteristics without restricting those belonging to

the generating individuals. Thus, the understanding

of the evolution of a topology through Genetic

Algorithms is interchangeable with the way that a

population becomes immune to a disease through

Natural Evolution. Once the operation mode for the

evolution of circuits has been defined, the Genetic

Algorithm can be inserted in platforms dedicated to

the evolution of circuits. Such platforms are called

Evolutionary Platforms and are classified according

to their mode of operation. Evolutionary Platforms

can be used to act on circuits and adapt them in the

event of faults. In this type of application,

reconfigurable platforms are able to restructure their

connections and devices used to achieve a desired

response, an answer that can be analog or digital.

These platforms are classified according to the type

of project carried out, the nature of the evolved

project and the operating structure of the platform.

The type of project carried out is classified

according to the objective of the evolution carried

out. This objective can be the optimization of an

existing circuit or the synthesis of a circuit topology

that satisfies a certain desired output. The

classification of the nature of the project refers to the

nature of the evolved topology. This nature

corresponds to the type of quantity observed at the

topology output and assesses whether the circuit has

an analog or digital output. These platforms are

classified according to the type of project carried

out, the nature of the evolved project and the

operating structure of the platform. The type of

project carried out is classified according to the

objective of the evolution carried out. This objective

can be the optimization of an existing circuit or the

synthesis of a circuit topology that satisfies a certain

desired output. The classification of the nature of the

project refers to the nature of the evolved topology.

Electronic Circuits Extrinsic Evolutionary Platform

753

This nature corresponds to the type of quantity

observed at the topology output and assesses

whether the circuit has an analog or digital output.

Regarding the operating structure, the classification

observes the evaluation method performed by the

platform. The evaluation of circuits can be carried

out internally or externally to the application

platform. When the evaluation process is carried out

internally, individuals are loaded directly onto the

platform and the suitability of each representation is

given by observing the response obtained when

leaving the platform. This type of platform receives

the classification of Intrinsic Platform. Such

technique guarantees an aptitude value perfectly

compatible with the final performance of the circuit,

since this aptitude is evaluated directly on the

hardware platform where the topology will be

applied. On the other hand, the assessment of

individuals can be performed outside the platform

where the topology will be applied. This evaluation

mode uses circuit simulators to evaluate the response

of each topology and assigns the fitness value

according to the simulated performance of each of

the representations used. This type of assessment

does not have an aptitude assessment as reliable as

the assessment of Intrinsic Platforms. However, the

use of simulators in the evaluation guarantees a

more flexible assessment of each evolved topology,

being more appropriate for experimentation.

Platforms with evaluation using simulators are given

the name of Extrinsic Platforms, that is, outside the

platform where they will be used. Thus, the mode of

operation of Intrinsic and Extrinsic platforms differs

only in the way of evaluating individuals of

evolution. Works focused on accurate knowledge of

the response after implemented, instead of a

simulated approach, normally use Intrinsic

Platforms. In this type of work, the system must

have a permanent interface with microcontrolled

hardware for the exchange of information.

2.2 Evolutionary Platform

The choice of the environment used in this paper

focused on the development of a platform that was

able to evolve topologies that used commercial

component values. In this way, the evaluation

process can be carried out in an agile way and the

response observed by the topologies is very close to

the actual response observed. The developed

platform consists of an extrinsic platform whose

circuit simulation will be performed externally by a

specific simulator. The chosen simulator was the

LTSpice XVII, developed by the integrated circuit

manufacturer Linear Technology Corporation, which

is now part of the multinational Analog Devices.

This choice is justified by the fact that it is a globally

used and distributed simulator, available for both the

Windows operating system and the MAC, and

because it is a free distribution software, not

requiring a license for its operation. The platform

will carry out the evolution of analog circuits, that is,

it will be a platform of an analog nature. In addition

to the classification as to the nature of the observed

response, there is a classification as to the nature of

the process carried out. As will be presented, the

platform will have three modes of operation. As the

platform evolves component values, it will be

classified as a topology synthesis platform. The

classification of topology synthesis is also given to

the way in which a complete change of components

and connections is carried out in order to obtain a

satisfactory topology. In the component addition

mode, there is an initially fixed topology that should

be improved by adding components. This mode that

seeks to correct the operation of a topology is

classified as a platform of an optimization nature.

The platform used for programming and,

consequently, responsible for making calls to the

simulator and operating the algorithm was

MATLAB, including GAOT (Genetic Algorithm

Optimization Toolbox). The communication

between GAOT, contained in MATLAB, and the

circuit simulator is carried out through netlist files,

that is, description files of circuit connections. In

this way, MATLAB will create the files with the

topologies to be tested and will read the files with

the sampling resulting from the simulation process.

So MATLAB will adjust the evolution parameters

and also exchange information between the Genetic

Algorithm - GAOT and the circuit simulator -

LTSpice. A structure of the developed platform can

be seen in Figure 1.

Figure 1: Flowchart of operation of the developed

platform.

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The fitness function to assess the evolutionary

adequacy is defined in equation 1 and 2.

(1)

(2)

In the chosen formula, the error between the desired

output and the output obtained by the evolved circuit

is considered in module and on average. Such an

assessment was used to ensure that the individual

value of each sample is considered as the final value,

preventing positive error values from being offset by

negative error values. Once the error is considered in

module, the suitability value will be a value between

zero and one. This restricted range of fitness allows

the use of both the roulette selection method and the

standardized selection method available by the

evolution algorithm used as a basis. In order to

connect the LTSpice simulator to MATLAB, calls

are made to the simulator in the background.

Simulator calls are made in the background for each

simulated circuit. Making calls in the background

frees the user from successive interventions in the

course of evolution. This type of call to the

simulator also allows the user to focus and monitor

the progress of the evolution. The simulator, despite

having a graphical interface, will use a text file

called netlist. Netlist files are simplified text files

that contain the representation of electrical circuits.

In these files, components are specified, their

respective values, the nodes to which they are

connected and basic syntax rules that define

simulation characteristics. MATLAB, the main

language used in the development of the system,

uses this standardized characteristic of the netlist

files to create them and transmit them to the circuit

simulator. The different topologies will be evaluated

according to their different characteristics and used

syntaxes.

2.2.1 Modes of Evolution of a Circuit

The developed platform has three evolution modes.

Each mode of operation will have a specific type of

chromosome and, consequently, a proportional

search space. The choice of the mode of operation

and the supply of the variables corresponding to the

mode are made before the execution of the evolution

process.

The first option is the Component Variation

evolution mode which is used in cases where the

topology to be evolved is fixed and only the value of

the components is evolved. In this evolution mode,

the user informs the fixed topology to the evaluation

function and the types of components used and the

evolution will seek the best set of values for the

desired output. In this evolution mode, the search

space is minimized since it is composed only by the

values of the components used.

The Component Add evolution mode is used in

cases where there is an initial fixed topology and the

evolution algorithm will add components to that

topology, with the goal of achieving the desired

output. In this evolution mode, the user informs the

topology attached to the evaluation function and the

types of components used. From there evolution will

seek a complementary topology that approaches the

desired output. The fixed topology will consist of a

structure similar to the first option -Variation of

components. The type of each component, defined

together with the fixed circuit, will also be used in

defining the new components added. After writing

the fixed circuit, the evolution algorithm will add

elements to it, up to a maximum number of elements

defined by the user before the evolution process. The

search space is considerably larger than the previous

mode. This increase in the search space is due to the

fact that positioning nodes are part of the evolution.

The last mode considered in this paper is the

Complete Variation evolution mode which is used in

cases where the user wants to perform a complete

evolution in search of a solution. In this mode, the

user informs the maximum number of components

to be used and the maximum number of nodes to

which these components must be connected. These

variables, together with the variables already used in

previous modes, such as the amount of component

values available, will determine the evolution to be

performed. From there, evolution will seek a

complete topology, which should approach the

desired output. The type of each component, as in

the previous modes, is defined by the user at a time

before the execution of the evolution. Based on the

types of components made available to the

algorithm, it will perform a complete evolution of

the topology, changing even the node defined as the

circuit output.

3 CASE STUDIES

After a discussion and considerations aspects

involving the development of the platform, case

studies were carried out to verify its operation. Such

analysis will be performed for each of the operating

modes that make up the platform and evaluated

Fitness

error

() ()

Goal Observed

Output i Output i

error

−



Electronic Circuits Extrinsic Evolutionary Platform

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according to the complexity of the circuits used. For

all case studies, the crossover rate used was 85%. A

large number of case studies were carried out from

which 3 were selected.

3.1 Case Study 1: Voltage Divider

The first circuit evaluated by the platform was a

basic voltage divider circuit. This circuit, seen in

Figure 2, will be used to confirm the platform's

operation and verify the impact of certain variables

on the evolution process.

Figure 2: Voltage divider circuit.

This evaluation will be carried out for the three

modes that make up the developed platform. Once

performed, it is possible to increase the complexity

of the evolved circuits and verify the evolution

behavior performed by the platform.

3.1.1 Evolution by Variation of Values

The first evolution was carried out in the variation

mode of component values where fixed values were

assigned to the parameters related to selection and

mutation and the evolution of individuals over the

generations was observed. The used parameters were

20 generations with 25 individuals. The crossover

rate was fixed at 0.85, the same as for the other

evolutions, and the selection rate for geometric

normalization and the mutation rate are fixed. Such

an evolution can be seen in Figure 3. Figure 4 shows

the topology obtained using 7 generations of 5

individuals.

3.1.2 Evolution by Adding Components

The evolution by Adding Components is used for

cases in which there is a fixed circuit in operation,

but in which the response does not correspond to the

desired one.

Figure 3: Evolution Using Fixed Mutation and Selection.

Figure 4: Topology Obtained Using 7 Generations of 5

Individuals.

Such an evolution mode can be understood as an

evolution to failures, in which an unwanted response

is observed in a circuit and the evolution algorithm

must add components to this circuit in order to

improve it. In the evolution of the voltage divider

topology, a resistor burn is simulated, which is no

longer part of the circuit (open circuit) and,

consequently, influences the obtained output. From

there, the algorithm will add components to the

topology and evaluate the addition in the presented

answer. At the end of the evolution, an additional

topology is sought that approximates the post-fault

response of the circuit to the previously desired

response. For this evolution a maximum of ten

components and five nodes will be used and all of

them will be resistors. Using the topology that

---------Best Result

-----------Average

OBSERVED FITNESS

GENERATIONS

Simulation History

20 Generations - 93.97 Fitness - 85 Ind.

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yielded the best performance in the evolution of this

circuit, a compatibility of 99.91% with the observed

response is obtained. This response and the evolved

topology can be seen in Figures 5 and 6 respectively.

Figure 5: Best performance evolution curve for voltage

divider in component addition mode.

Figure 6: Evolved topology for voltage divider in

component addition mode.

3.1.3 Evolution by Complete Component

Change

The next evolution mode corresponds to the

complete component change mode. In this way,

alternative topologies are sought through the

complete variation of the variables in question, that

is, topologies not known directly to the problem.

The topology with the shortest evolution time,

highest performance (Fitness of 100 %) was

considered the best result depicted in figure 7.

3.2 Case Study 2: Amplifier

After performing the evolution for the voltage

divider circuit, the algorithm is used to evolve the

topology of an amplifier circuit. Such evolution aims

to observe if the algorithm is capable of evolving

topologies for more complex circuits. A basic

topology of an amplifier circuit can be seen below in

Figure 8.

Figure 7: Evolved topology for voltage divider in full

evolution mode.

Figure 8: Basic amplifier circuit for case study 2.

3.2.1 Evolution by Variation of Values

The first evolution carried out consists of the

evolution of values of the basic topology of the

amplifier circuit of gain 3. Figure 9 and 10 shows

the evolution curve and the evolved topology

respectively.

Figure 9: Evolution curve for case study 2 – variation of

values.

Observed Fitness

_____

Best Result

______ Average

Generations

20 Generations, Fitness: 99.91 % - 25 individuals

_____ Best Result

______ Average

Observed Fitness

GENERATIONS

40 Generations, Fitness 99,99 %, 65 Individuals

Electronic Circuits Extrinsic Evolutionary Platform

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Figure 10: Evolved topology for case study 2 in variation

of values mode.

3.2.2 Evolution by Adding Components

After performing the evolution of a topology for the

amplifier circuit, we will simulate a failure situation

in the evolved circuit. This simulation will test the

platform for a solution to the failure using the

Component Addition mode. The simulated fault will

be the burning of one of its resistors, more precisely

the resistor with a value of 180 kΩ. After the

burning of this resistor, the connection of the nodes

of this component is considered to be non-existent.

From the resulting circuit, the algorithm will add

components to the topology and evaluate such

addition in the observed response. At the end of the

evolution, an additional topology that is capable of

correcting the operation of the amplifier circuit is

sought. It is possible to observe that the evolution

accomplished obtained in response the topology

previously used. Such response was obtained by

adding another resistor with the same value as the

burnt resistor. The evolution curve and the evolved

circuit are depicted in figures 11 and 12 respectively.

Figure 11: Evolution curve for case study 2 – adding

components.

Figure 12: Evolved topology for case study 2 in adding

components mode.

3.2.3 Evolution by Complete Component

Change

After the evolution by adding components, the change in

the Component Modification mode will take place. In this

evolution, the entire topology will be evolved in search of

an adequate solution. This search may not use a starting

solution, which in this case will be the topology evolved in

the evolution of values. The evolved topology and its

observed response at the end of the evolution process can

be seen in Figures 13 and 14 respectively.

It can be seen

that the evolved circuit does not resemble the topology

known for the circuit. However, the output presented by

this topology is very close to the desired one. This

indicates that the platform is capable of searching for

unusual topologies that satisfy a certain desired output.

Figure 13: Evolution curve for case study 2 – complete

components change.

GENERATIONS

___

Best Result

___Average

– Fi

ess

99.

–

dua

ERATI

ONS

80 Generations – Fitness 94.41% - 65

individuals

Best Result

Avera

Obse

ved

Fitn

ess

Obse

ved

Fitn

ess

_____

Best Result

______ Average

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Figure 14: Evolved topology for case study 2 in complete

components change mode.

4 CONCLUSIONS

The results achieved for each of the existing modes

of operation, with different circuits of complexities

and types of different analyzes, indicate good

flexibility for the most diverse experiments. The

designed platform allows the designer to quickly and

easily change the specification of the circuit to be

evolved without complex changes in the code.

Changing the topologies analyzed, the inputs used

and the type of input can be easily modified from the

information passed to the platform. It is the role of

the platform to create the files necessary for

communication with the simulator and the files

resulting from this evolution, without the need for

user interference. In order to optimize the evolution

process, reducing the processing, all case studies

were performed using a virtual disk in the

computer's RAM memory. This type of disk is

called RAM Disk and optimizes the speed of access

and writing of files during accesses. This

optimization occurs because the disk works at the

speed of the respective RAM used, which is

considerably higher than the respective speeds on a

conventional hard drive. Regarding possible future

work it is suggested studies with the objective to

continuously identify faults in circuits. Through this

identification, it would be possible to make

automatic calls to the developed platform and,

consequently, the evolution with automatic circuit

repair from these failures. Another possibility is the

use of topologies evolved in reconfigurable circuits.

In this way, the results evolved by the platform

would be automatically loaded in such circuits and,

consequently, would be available for use in

automatic mode. Based on the performance of

virtualized disks over time another possibility to

further increase performance would be the use of

graphics processing units (GPUs) to optimize the

processing of these circuit evolutions. Such units,

available on video cards, can decrease the time

needed to carry out the evolutions studied in this

paper and in the evolutions of more complex

circuits. The main contribution of the developed

platform is the possibility of changing each

parameter used. Such capacity enables detailed

future studies on the impact of each parameter on the

evolution of circuits.

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