EMACI: A Visual Interface of Multi-Parameters for Interactive Evolutionary Algorithm
Kisa Ohyama*, Takayuki Itoh*, Fumiyoshi Yamashita**, Koji Koyamada****Graduate School of Humanitics and Sciences, Ochanomizu University**Graduate School of Pharmaceutical Sciences, Kyoto University***Center for the Promotion of Excellence in Higher Education, Kyoto University

ABSTRACTThis paper proposes a technique for visualizing the
progression of evolutionary algorithms such as genetic
algorithms (GAs). Our technique supposes that a GA solves
optimization problems that maximize target functions withmultiple parameters. Then, our technique presents the timevarying parameters during the progression of the GA. The
technique applies two-tone pseudo-coloring for the precise
representation of changes in parameter values, so that users
can understand the behavior of the GA. We also provide auser interface to control the configuration of the GA so thatusers can obtain the optimal solution earlier. The paper shows
experimental results to demonstrate the efficacy of thetechnique.

1. INTRODUCTION
An Evolutionary Algorithm (EA) is a very powerful
optimization scheme that has been applied to various technicalfields. A Genetic Algorithm (GA), one of the most famous EA
schemes, is especially useful for problems that lack clear
solutions and that cannot be solved using full search schemes
because the GA’s parameter space is huge and unstructured. A
GA evaluates the fitness of an organism to an environment [1],based on principles of Darwin’s evolutionary theory. Supposethat the genes of animals correspond to the input parametersof certain functions, and the fitness values correspond to thefunctions’ return values. Here, the GA gradually discovers the
optimal solution of the problem according to the evolution of
the input parameters.
The following points are important to quickly discover
optimal solutions using a GA:•
Coding input parameters as genes, and•Controlling configurations for evolution of genes.
We especially need to effectively and carefully configure GAs
while observing the exploration of parameter spaces according
to the evolution of genes. However, it is generally difficult tofully automate controling the configuration of GAs. Therefore,
several studies focus on interactive implementations of GAs toeffectively control the exploration of parameter spaces.
This paper proposes EMACI (Evolutional Multi-parameter
Analysis and Control Interface), a visual interface of theprocess of GAs. Regarding the parameters and target function
as a multi-dimensional data, EMACI represents the repetitive
process of a GA as time-sequence multi-dimensional data.
EMACI also provides a user interface to effectively control
the configuration while users observe the visualization results.
This user interface helps users to control the configuration
intuitively, and consequently to discover optimal solutions inshorter times.

The naming of EMACI comes from “Emakimono”, handscrolled, horizontally illustrated narratives created during
Heian, Kamakura, and Muromachi periods in Japan; we feelthe technique looks like Emakimono since it displays the time
sequence of multi-parameters as a horizontal spread.
This paper also introduces an application of the EMACI to
the parameter optimization of heart cell simulations. This
optimization problem defines the target function as the errorof simulation result of time-varying electric potential against
the real measurement, with parameters as density or othermeasurements of ions such as K+ and Na+. We need to repeat
the simulation inputting a variety of parameters, in order todiscover the optimal solution [2]. We are applying EMACI tothis optimization problem, by coding the measurements ofions as genes, and by repeating the simulation with theevolution of the parameters by the GA.

2. RELATED WORK
2.1 Genetic Algorithm
A Generic Algorithm (GA) is an optimization scheme
inspired by Darwin’s evolutionary theory, which consists ofseveral processes mimicking evolutionary processes ofanimals, including selection, crossover, and mutation [1]．

Figure 1: Processing flow of a GA.
Figure 1 shows a typical processing flow of a GA. It consistsof the following key processes:
Initialization: randomly generates initial genes.
Evaluation: calculates the fitness values for each gene, andsorts the genes according to the evaluation result.
Selection: extracts two parents, where their probabilities areweighted according to the evaluation results.
Crossover: generates children genes from the selected two
genes after the Selection process. The GA repeats the
Selection and Crossover processes until it generates the
specified number of children. Here we may apply “elite”

reproduction, which just copies the genes of highly fit parents
to their children as clones.
Mutation: randomly modifies the genes on a given
probability. This operation corresponds to a failure to
expresscertain DNA. Mutation is a useful technique to breakout local optimal solutions and to get closer to the global
optimal solution.
Terminate Check: stops the repetition of the GA according topredefined conditions.
This implementation mimics the gametogony of animals,
during which the exchange of genes of selected individuals
results in more conformable children. The repetition of GA
processes leads to the optimal solution of target functions.
2.2. Visualization for Genetic Algorithm
Many studies involving GAs have attempted to analyze thetrajectory of parameter spaces and the evolution of targetfunctions, to understand the effort of methodology andparameters for the quick and certain exploration. It is effective
to display the evolution of target functions as time-sequence
data. However, many GA-applied optimization problems havemultiple parameters. Therefore, it is generally difficult tounderstand the relations between the parameters and the targetfunction, if one simply draws them as polygonal charts.
Visualization techniques are desirable so that we can visually
understand the exploration of parameter spaces and theevolution of GA processes.
Moreover, it often happens that the GA falls into local
optimal solutions from which breaking out is difficult.
Visualization of parameter space exploration is useful tounderstand the causes of the falls and to discuss the strategy to
break out of the local optimal solutions.
Interactive, visual implementations of GAs are useful sinceusers can control the exploration of parameter spaces while
they observe the repetition of the GAs.
The performance of the parameter space exploration of a
GA depends on not only on methodology but also on many
configurations, such as the number of individuals, theprobability of mutation, and the probability of crossover. It is
generally difficult to develop conclusive techniques tocompletely, automatically control the configuration. Theinteractive implementation of a GA is useful because one cancontrol the configuration via a user interface, so that one canthereby improve the parameter space exploration.
Visualization of GA processes helps to support configuration
control.
There are several recent works on the visualization of GA
processes from the above viewpoint. The following are
examples of related works on the visualization of GA
processes:• Visualization of multi-parameter spaces using Self
Organizing Map (SOM) [3]．The technique represents
clusters of solutions to easily understand the explorationof the space. The technique has been applied to somemulti-criterion optimization problems.
• Visualization of solutions led by Interactive Evolutionary
Algorithm (IEA) and Interactive Genetic Algorithm(IGA) [4]. The technique enables subjective control ofGAs, since users interactively evaluate the solutions.
• Visualization of the landscape of target function values[5]. The technique codes multi-dimensional parameters
as input vectors, and summarizes them into a 2dimensional display space. It can represent the
distribution of parameters during the repetition of a GA

process.
Against the above existing work, our technique focuses on the
representation of time sequence changes in parameter values
for real-time adjustment of configurations of GA.
2.3. Multi-Dimensional Data Visualization
As mentioned above, many GA-applied problems optimize
target functions of multi-dimensional parameters. Therefore,
the visualization of parameter spaces corresponds to thevisualization of multi-dimensional time-sequence data.
The following are typical multi-dimensional datavisualization techniques. Many of such famous multidimensional data visualization techniques originally deal withstatic data, and visualization of time-varying multidimensional data is still a hot and changing topic.
•
Parallel Coordinates [6]. The technique draws verticallines in a 2-dimensional orthogonal coordinate system,
where each line corresponds to each dimension, andgenerates polygonal charts by plotting values of the
input information onto the vertical lines.
•
Worlds within Worlds [7]. The technique assigns 3
variables of the input data as each axis of 3-dimensional
orthogonal coordinate system, and plots each data item.
It then assigns other 3 variables as each axis of a 3dimensional orthogonal coordinate system located inside
a part of the first world.
•
Several well-known techniques project the input multidimensional data onto 2- or 3-dimensional spaces using
dimension reduction techniques. Design Galleries [8] isa well-known technique applying such dimension
reduction technique.
•
Several techniques apply “Glyphs” to represent multidimensional values by varying their shapes, sizes, andcolors. Ebert’s technique [9] is an example of multidimensional data visualization using Glyphs.
2.4. Two-Tone Pseudo Coloring
The technique proposed in this paper applies two-tone pseudocoloring [10]. It assigns two colors to a one-dimensional range,
and represents the value by controlling the widths of the twocolors. It is very suitable to precisely represent long-scale
time-sequence data in a small display space. Also, it
represents both the overview and details of the dataset in one
image.
Figure 2 illustrates the visualization of time-sequence data by
two-tone pseudo coloring and traditional polygonal charts. In
the final phase when the data slightly changes, two-tone
pseudo coloring captures the fine details. In contrast, it would
be difficult to recognize the changes in the traditional
polygonal chart.

Figure 2. (Left) Visualization by two-tone pseudo color.
(Right) Visualization by traditional polygonal chart.

3. EMACI: THE PROPOSED TECHNIQUE
Let us define a target function of an optimization problem as:s = f ( x1 , x2 ,..., xn ) …(1)

where we would like to discover the maximum s value and theset of parameters x1 to xn . Here EMACI visualizes the
progression of a GA and provides a user interface tointeractively control the configuration of a GA.
Processing flow of EMACI is as follows:
1. Initialize the GA.
2. Perform selection, crossover, and mutation, as shown in
Figure 1.
3. Evaluate the genes and specify the maximum s value,
and x1 to xn which produce the maximum s .
4.

Visualize x1 to xn using two-tone pseudo coloring.

5.

Perform terminate check, as shown in Figure 1, andterminate if the progression satisfies the conditions.
Occasionally pop-up a dialog window to control theconfiguration of the GA, and close the dialog after awhile (i.e., 10 seconds later).
Return to 2.

6.

7.

x1

Figure 4. Example of a dialog window to control theconfiguration of GA.

x2

4. EXAMPLE

x3

We implemented EMACI in Java 1.5, and executed it on an
IBM ThinkCentre with Windows XP. Our current
implementation defines 6 colors C0 to C 5 , which are blue,x4
Generation
Figure 3. Visualization of parameters.
Figure 3 shows an example of visualization of 4 parameters
during the progression of a GA. The horizontal axis denotes
the generation of the GA, and colored belts represent thechanges in parameter values. Here we represent the parametervalues as follows, similar to that which is described in [10]:•Specify the range of a parameter xi as [min i , max i ] .
•Define intervals Bi = (max i − min i ) / m .

•Let Aij = min i + jBi .

•Define (m + 1) colors C0 to Cm .

•-parameter for a more stable progression and accurate
solution.
EMACI provides a dialog window to change the configurationof the GA so that users can interactively employ their acquired
knowledge from the visualization. Figure 4 is an example ofthe dialog window. Our implementation can control thefrequency of the pop-up of the dialog window: pop-up forevery generation, once in several generations, only when
every parameter is stable for long generations, and so on.

For each generation,
Specify j that satisfies Aij < xi < Ai ( j +1) .
Paint the vertical line segment at xi with two colors

C j and C( j +1) . Divide the line segment into two parts
so that the ratio of the length of the lower part is( xi − Aij ) / Bi , and use C j for the upper part andC( j +1) for the lower part.
This visualization represents changes in parameter values as
the changes of combinations or widths of two colors. It can
inform users of slight changes in parameter values, as
discussed in Section 2.4. Users can acquire a great deal of
information from the visualization, for example:•
Even if every parameter is stable in long generations,
one may not be sure whether the progression has alreadyarrived at the global optimal solution. One may want toencourage mutations to drastically move the parameter
space.
•
A specific parameter is always stable in wholegenerations. One may want to narrow the range of thesky blue, moss green, yellowish green, bright yellow, and red.
Figure 5 denotes the colors and ratio of their lengths in our
implementation.

min

max
Figure 5. Colors used in our implementation.

As a simple example problem, we also developed a targetfunction which requires 10 parameters and contains 6 local
optimal solutions. Ranges of all parameters are 0.0 and 1.0.
Return values of the target function are normalized so that thereturn value of the global optimal solution is 1.0.
Figure 8 shows an example of the visualization of the time
sequence of 10 parameters during the GA process. The upper
part of the figure shows the best parameters during 1 to 75
generations, and the lower part shows them during 76 to 150
generations. The figure denotes that the values of the bestparameters drastically changed during generations1 to 40, thenstayed during generations 40 to 90 , and slightly changed
several times during generations 90 to 150.
When the best parameters stay during long generations, it isoften better to change the configuration of the GA. Ourcurrent implementation pops-up the dialog window shown in
Figure 4, when the best parameters do not change during 10
generations. In this experiment we changed the following
configurations: the probability of crossover, the probability ofmutation, and the percentage of elites.
Table 1 shows the statistics of the GA results based onchanging configurations during the repetition of the GA
process. In this table “Global optimal” denotes the ratio ofresults that GA arrived around the global optimal solution, notlocal optimal solutions, where larger values are better.
“Average” denotes the average of the best values of the targetfunction, where larger values are better. The results that theinteractive changes in configuration values brought better
results: a change in the probability of crossover and thepercentage of elites brought better values of target functions,and the probability of mutation brought a better ratio
approximating the global optimal solution. We would like tomore carefully analyze the effect of interactive change infuture work. We also would like to have more experimentswith more complicated target functions, additional
configuration changes, and non-expert examinees.
Table 1 statistics of GA results changing the configurations.
Interactive change
Global optimal
AverageNone
0.48
0.9781
Probability of crossover
0.52
0.9911
Probability of mutation
0.88
0.9729Percentage of elites
0.60
0.9817channel, the kalium channel, the calcium channel, and so on.
Figure 6 briefly denotes the mechanism.
The cell simulation model [2] calculates the temporal valueof the action potential of the cell, and its error against the realmeasurement. Our approach treats the error as the targetfunction, and the related values of ions—such as electric
current—as parameters. EMACI visualizes the exploration of
the values of ions to discover the optimal solution of the cell
simulation.
Figure 7 shows an example of a visualization of a GA
process for heart cell simulation. Here our implementation
applies 4 parameters shown in Table 2 during the GA process.
This figure shows that parameters were drastically changing
during the early stage of GA process, but that the parameter
space stabilized during the later stage.

5. APPLICATION TO CELL SIMULATIONThis section introduces an application of EMACI to theparameter optimization of heart cell simulation [2].
The human body is a very complicated mechanism, andanalyzing it is therefore a difficult problem. Recently there
have been many studies to compute the biological functions,
including organs, blood circulation, and energy vicissitude, toclarify this complicated mechanism. Cell simulation is anactive research topic, because the cell is the smallest and mostfundamental unit of human body.
We are applying EMACI to a heart cell simulation model[2]. The model is designed so that it realistically reacts
according to an environmental change or an external impulse.
We expect that such computer simulations can contribute not
only to clarify the complicated mechanism of the human body,
but also to improve advances in medical treatments and drug
design.
The heart supplies blood to the entire body during the life ofa human. This organ is comprised of muscular pumps, and it
continues to work thanks to action potential and to ionsaround heart cells. Pulses of the action potential are generated
approximately once per second, and then densities of the ionsdrastically change according to the changes of the action
potentials. These pulses cause the heart muscles to contract,pushing blood outside the heart. All the heart cells work
together for this mechanism to make a heartbeat.

Figure 7. Example of a visualization of a GA process for heartcell simulation.
Table 2. Parameters applied in our implementation.
Name
Description
Range (pA)ICaL Voltage-dependent L-type Ca+[4500.0, 5500.0]
channel.
IK1
Inwardly-rectifying K+[1.0, 3.0]
channel.
IKr
Quickly-delayed-rectifier K+[0.025, 0.045]
channel.
IKs
Slowly-delayed-rectifier K+[0.01, 0.05]
channel.

6. CONCLUSTIONThis paper presented EMACI, a technique for the visualizationof GA processes. It represents the time sequence of multidimensional parameters by applying two-tone pseudo coloring,so that users can easily look over the change in parametervalues during the GA processes. It also provides a userinterface to flexibly control the configuration of the GA
process. The paper provided the results demonstrating theeffect of EMACI, as well as an application to cell simulation.
Our future work will include the following issues:•
Feasibility tests using target functions which requirelarge numbers of parameters.
•
Experiments with additional configuration changes, suchas changes in ranges of parameters.
•
Experiments with non-expert users.
•
Proof of the contribution of EMACI to the cell
simulation.
•
Combination with other visualization techniques [3,4].

Figure 6. Mechanism of ion channels. (from [11]).

ACKNOWLEDGEMENTS
Changes in densities of ions result in the pulses of action
potential. Here, ion channels of cell membranes work as doors
to control the densities of ions. Cell membranes themselves do
not allow for the transport of ions; however, the ions cantransport when so-called ion channels open. Each ion channeltransports only its specific ion: therefore, the ion channels are
categorized according to the specific ion, such as the natrium

We appreciate Prof. Nobuaki Sarai, Dr. Lu Jianyin, and Ms.
Yukiko Yamashita, for their advice on heart cell simulation.

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http://www.med.akitau.ac.jp/~yakuri/km_home/index_jp.html

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x1
x2
x3
x4
x5
x6
x7
x8
x9
x10
90
x1
x2
x3
x4
x5
x6
x7
x8
x9
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Figure 8. Example of visualization of time sequence of 10 parameters during the GA process.