2019 International Conference on Computational Science and Computational Intelligence (CSCI)
Computational Science and Intelligence Programming in Business with
Genetic, Generic Algorithms and AI Software Products:
An Adaptive Algorithmic Approach
Anastasia-Dimitra Lipitakis1 and Evangelia A.E.C. Lipitakis2
(1)
Department of Informatics and Telematics, Harokopio University, Athens, Hellas
adlipita@hua.gr
(2)
Kent Business School, University of Kent, Canterbury, England
leacynthialipitakis@yahoo.com
connections between job performance and certain
attributes by applying ML models and analytics
methods. The resulting search results can be matched
the intents of job seekers and special software can be
applied by doing searchers on the related pages of
internet. The optimal result of algorithmic and ML
methodologies usage is to match job applicants to the
available jobs in a better way than actual human
endeavor can do.
Abstract
In this research study the topic of Artificial
Intelligence and Business is examined. AI
methodologies and their usage in several
organizations in various applications concerning
staffing, careers etc. and hiring predictions are
discussed. The concept of genetic and generic
algorithms with their several important aspects are
discussed and various related applications in a wide
spectrum of topics are presented. Various genetic
programming
implementations
with
the
corresponding related software products are given
and several AI software packages, genetic
algorithmic methodologies and related applications
are presented.
2.
Artificial Intelligence Algorithms
and Business
In recent times the field of Artificial Intelligence
(AI) has significant impact on financial services
sectors and global financial markets comparing with
sophisticated classes of algorithms for trading by
creating a new interesting applied research topic for
traders and regulators (McGrath, 2016). It is reported
that algorithmic trading systems are handling about
75% of the volume of global trades (Thomson
Reuters, 2016-2017) with industrial predictions
owing a constant increase mainly due to the
following reasons: (i) huge product demand and
several innovations, (ii) greater automation of trades
in several asset classes (EU-MiFID II), (iii) retail
trading market expansion worldwide opening new
algorithmic trading and increasing demand for
technological advancement.
Algorithmic procedures can be efficiently used
for computing, data processing, automated reasoning
and sophisticated algorithmic applications play a
significant role in sifting through masses of data and
have a widespread use in everyday life (Hickman,
2013). Note that certain computer algorithms are
designed for allowing computers to learn on their
own facilitating machine learning (ML) and
including data mining, pattern recognition and other
related applications (Finley, 2014). The inventor of
World Wide Web (WWW) stated characteristically
that ‘Data is a precious thing and will last longer than
the systems themselves’.
Keywords and Phrases: Artificial intelligence,
businesses,
algorithmic
methodologies,
AI
applications, evolution process, generic algorithms,
genetic algorithms
1. Introduction
Artificial Intelligence (AI) software provides
appropriate solutions for several organizations and
businesses by speeding processes of online
simulations of what software includes multiple
choice questions and captures several human
qualities using natural language processing and
machine learning (ML) methodologies for
constructing psychological profiles of suitable
candidates.
ML methodologies and related predictive
algorithms can be used as tools for identifying best
candidates and assessing certain human qualities by
analyzing special characteristics such as word choice,
gestures, emotional traits, facial expressions, body
language, social media posts etc. Furthermore,
human judgment, face to face interaction and
personal communication can be used. Better
communication with potential applicants can be
achieved by special AI programs allowing the
scanning of millions of job openings for uncovering
978-1-7281-5584-5/19/$31.00 ©2019 IEEE
DOI 10.1109/CSCI49370.2019.00058
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3. Genetic Algorithms and Applications
such as Ada, C#, Delphi, Eiffel, F#, Java, ObjectiveC, Visual Basic and others. Generic programming
follows the idea of abstracting from concrete,
efficient algorithms to obtain generic algorithms that
can be combined with different data representations
in order to produce a wide variety of useful software.
Genetic algorithms can be efficiently used for
computing solutions of complex search problems.
They can be used in several fields, such as
engineering for creating incredibly high quality
products due to their ability to search a through a
huge combination of parameters to find the best
match. They can search through different
combinations of materials and designs for finding the
perfect combination of both which could result in a
stronger, lighter and overall, better final products.
They can also be used for designing computer
algorithms, scheduling different tasks, and solving
other optimization problems. Genetic algorithms are
based on the process of evolution by natural selection
which has been observed in nature. They can
basically replicate the way in which life uses
evolution for computing solutions to real world
problems. They are currently considered to be
prominent computational models of evolution in
artificial life systems. These decentralized models
can be used as basis for understanding other complex
systems and phenomena in the world. The generic
algorithms can be used for studying how learning and
evolution processes interact and furthermore for
modelling various interesting systems, such as
ecosystems, immune systems, cognitive systems and
social systems (Winder et al., 1995; Whitley and
Vose, 1995; Louis, 1993).
Genetic algorithms can be efficiently used on
mixed (continuous and discrete) combinatorial
problems, but they tend to be computationally
expensive. By using genetic algorithms, the solution
of the original problem is represented as a genome
(chromosome or strings of ones and zeros or bits
[genes]) and then a population of solutions is created
and genetic operators (mutation, crossover,
inversion) are applied for evolving the solutions in
order to find the best ones. Genetic algorithms can be
efficiently used by considering the following
important aspects: (i) definition of objective
functions, (ii) definition and implementation of
generic representations and (iii) definition and
implementation of generic operators. Several
variations for improving performance, computing
multiple optima and parallelizing these algorithms
can be tested for solving the considered problems
(Grefenstette and Baker, 1989; Altenberg, 1994;
Vose, 1993). The main difference between genetic
programming and genetic algorithms is the
representation of solutions, i.e. genetic programming
creates computer programs following the schemes of
computer languages as solutions, while genetic
Genetic algorithms (GA) differ from classical
algorithms (CA) (derivative based optimization
algorithms) in the following: (i) GA generate
populations of points at every iteration, with the best
point approaching optimal solutions, while CA
generate single points at every iteration with
sequences of points approaching optimal solutions,
(ii) GA select the next populations by computing
which use random processes, while CA select next
points in sequences by deterministic computations.
Genetic algorithms are heuristic searches that mimic
the process of natural selection using basic methods,
such as mutation and crossover for generating new
genotypes aiming to computation of acceptable
solutions to given problems. ML algorithms can be
used for improving the performance of genetic and
evolutionary algorithms (Zhang et al., 2011).
Genetic algorithms can be efficiently used on
mixed (continuous and discrete) combinatorial
problems, but they tend to be computationally
expensive. By using genetic algorithms, the solution
of the original problem is represented as a genome
(chromosome) and then a population of solutions is
created and genetic operators (mutation, crossover)
are applied for evolving the solutions in order to find
the best ones.
3.1 Genetic and Generic Algorithms
In computer science and operations research, a
genetic algorithm is a metaheuristic procedure
inspired by the process of natural selection that
belongs to the larger class of evolutionary
algorithms. Genetic algorithms are heuristic search
methods used in artificial intelligence and computing.
They can be used for finding optimized solutions to
search problems based on the theory of natural
selection and evolutionary biology. Genetic
algorithms are excellent for searching through large
and complex data sets. They are considered capable
of finding reasonable solutions to complex issues as
they are highly capable of solving unconstrained and
constrained optimization issues. The genetic
algorithm repeatedly modifies a population of
individual solutions. At each step, the genetic
algorithm selects individuals at random from the
current population to be parents and uses them to
produce the children for the next generation. Generic
programming is a style of computer programming in
which algorithms can be written in terms of types to
be specified later. This programming approach
allows writing common functions or types that differ
only in the set of types on which they operate when
used, thus reducing duplication. These software
entities are known as generics in computer languages,
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algorithms create strings of numbers that represent
solutions (Whitley and Vose, 1995; Mitchell, 1999).
Genetic algorithms can be considered as weak
optimization methods and their search procedures
can be used for solving a wide range of
computational problems without any domain specific
knowledge. These search procedures are based on
mechanics of natural selection and natural genetics
using various evolution theories as tools for solving
computational problems in science and engineering.
This can be achieved by evolving populations of
candidate solutions to particular problems using
operations based on natural selection and natural
genetic variation (Goldberg, 1989).
A wide spectrum of genetic algorithms
applications are included in various scientific,
information science topics in business such as
bankruptcy predictions, currency demand analysis,
control design, craud detection, interest earnings
maximization, money laundering identification, plant
layout, portfolio optimization, process control, risk
analysis, space allocation for ROI, supply chain
optimization, resource allocation, data modeling and
analysis, stock market risk analysis, broker
surveillance, routing, sports event attendance
prediction, complex equipment design, job shop
scheduling, signal processing, vehicle scheduling,
worker scheduling and job schedule developments.
significant complexity. These computational
techniques can lead to optimized solutions of certain
complex problems (McCall, 2005). Several genetic
programming
implementations
with
the
corresponding related software products are given.
Genetic programming implementations can be
achieved by several languages and their variants,
such as it is shown in Table 1.
4. The implementation of a simple Genetic
Algorithm
Genetic algorithms are heuristic modelling
techniques inspired be natural evolution and can be
efficiently applied to wide spectrum of real world of
significant complexity. These computational
techniques can lead to optimized solutions of certain
complex problems (McCall, 2005).
Let us consider a given well defined given
problem for solution and a bit string representation
for candidate solutions. Then a simple genetic
algorithm can be presented as follows:
Algorithm GA-1 (εRP RPC, εFC FCP, εPC PCCP,
εCR CRFO, εTC TCNP, εRPN RPNP, εUF FPBC)
Purpose: This algorithm describes the population of
bit chromosomes
Input: randomly generated population of (n-1) bit
chromosomes (RPC), fitness f(x) of each
chromesome x in the population (FCP), pair
chromosomes from current population with selection
probability being an increasing function of fitness
(PCCP), crossover probability cross over the selected
pair at a randomly chosen point (chosen with uniform
probability) to form two offspring (CRFO), transfer
the resulting chromosomes in the new population
(TCNP), replacement of current population with the
new population (RPNP), predetermined real
parameters εRP , εFC , εPC , εCR , εTC , εRPN and the
undetermined factor parameter εFP .
Output: final population of bit chromosomes (FPBC)
Computational Procedure:
Step 0: consider the adaptive parameters εRP, εFC,
εPC,, εCR , εTC , εRPN .
Step 1: chose a randomly generated population of (n1) bit chromosomes (i.e. candidate solutions) εRP
(RPC)
Step 2: compute the fitness f(x) of each chromesome
x in the population εFC (FCP).
Step 3: repeat the following iterative steps until noffspring have been created:
Step 3.1: chose pair chromosomes from
current population with selection probability being an
increasing function of fitness εPC (PCCP).
/*Note that the selection process can be done with
replacement, i.e. the same chromosome can be
3.2 Genetic Programming Implementations
and Software Products
In the latter work the two basic conceptions of
universe, i.e. the rational mechanics of Newton and
Galileo, and the advanced electromagnetic theory,
i.e. Maxwell, Hertz and Lorentz, are discussed.
Genetic Programming algorithms are evolutionary
algorithms based on methodologies inspired by
biological evolution for finding that can perform
several user defined tasks. These algorithms are
machine learning techniques used for optimizing
populations of computer programs according to
fitness landscape determined by programming
abilities for performing predetermined computational
tasks. The term Meta-Genetic Programming refer to
meta-learning techniques of evolving genetic
programming systems using generic programming
itself. The process is a recursive terminating
algorithm that allows avoiding infinite recursion
(Schmidhuber, 1987).
Genetic Algorithms Applications
The wide spectrum of AI applications and the
universal applicability of algorithmic methodologies.
Genetic algorithms are heuristic modelling
techniques inspired be natural evolution and can be
efficiently applied to wide spectrum of real world of
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Genetic
Language
MATLAB
Implementation
Description
Reference
GPLAB
GPTIPS
Silva, 2007
Searson et al., 2010
Python
Pyvolution:
A Genetic Programming Toolbox
Genetic Programming & Symbolic Regression Toolbox,
aiming at performing multigene symbolic regression
modular extensible evolutionary algorithms framework with
complete documentation
Java
deap:
pySTEP:
Pyevolve
EvoJ:
JEF
GenPro:
Distributed Evolutionary Algorithms
Python Strongly Typed gEnetic Programming
JAGA
JGAP
n-genes:
PMDGP
MOEA Framework:
Java GALib:
PushGP:
Groovy:
GEVA:
jGE:
JCLEC:
C
C++
Watchmaker Framework:
TinyGP:
LAGEP
RobGP:
GPC++:
GeneXproTools:
RMIT GP:
PMDGP:
GAlib:
ECF:
MicroGP (uGP):
C#
EOEvolutionary
Computation
Framework:
BraneCloud Evolution:
HeuristicLab
GPdotNET
Prolog
Ruby
DCTG-GP:
DRP
Perl
.NET
PerlGP:
GPE
Evolutionary computations framework
JAVA Evolution Framework
Reflective Object Oriented Genetic Programming. Open
Source Framework.
Extend with POJO's, generates plain Java code
Extensible and pluggable open source API for implementing
genetic algorithms and genetic programming applications
Java Genetic Algorithms and Genetic Programming, an
open-source framework
Java Genetic Algorithms and Genetic Programming (stack
oriented) framework
object oriented framework for solving genetic programming
problems
Multiobjective Evolutionary Algorithm Framework
Source Forge open source Java genetic algorithm library,
complete with Javadocs and examples
a strongly typed, stack-based genetic programming system
that allows GP to manipulate its own code
Groovy Java Genetic Programming
Grammatical Evolution in Java
Java Grammatical Evolution
Evolutionary Computation Library in Java, expression tree
encoding, syntax tree encoding
Genetic Computation Framework
A tiny genetic programming system
Supporting single/multiple population genetic programming
to generate mathematical functions. Open Source, OpenMP
used
Robust Genetic Programming System)
Genetic Programming C++ Class Library
Commercial gene expression programming software for
logistic regression, classification and regression
A Genetic Programming Package with support for
Automatically Defined Functions
object oriented framework for solving genetic programming
problems
Object oriented framework with 4 different GA
implementations and 4 representation types
Evolutionary Computation Framework. different genotypes,
parallel algorithms
General purpose tool, mostly exploited for assembly language
generation)
C++ with static polymorphism
Fork of ECJ for .NET 4.0
A Paradigm-Independent and Extensible Environment for
Heuristic Optimization, rich graphical user interface, open
source, plugin-based architecture
Codeplex open source project for modeling and optimization
based on Genetic programming).
Grammar-guided GP using logic-based attribute grammars
Directed Ruby Programming, Genetic Programming &
Grammatical Evolution Library
Grammar-based genetic programming in Perl
Framework for conducting experiments in Genetic
Programming
Table 1: Genetic Languages: Implementation and description characterists
Panchapakesan,
2012
Python
Software
Foundation
Fortin et al., 2012
Khoury, 2009
Perone, 2014
Keijzer et al., 2002
Cheong, 2011;
Le Goues C., 2011
Weise, 2009
Van der Meulen et al.,
2001
Zhang and Li, 2007
Java-GAlib, 1996
Spectorand Helmuth, 2014
GEVA, 2008
Georgiou
2006
and
Teahan,
Ventura et al.,2007
Dyer, 2010
Poli et al., 1999
Baily et al., 2012
Schmieder, 2014
Ferreira, 2006
RMIT-GP Doc., 2004
Van der Meulen et al.,
2001
Wall, 1996
Georgopoulos et all., 2009
Keijzer et al., 2002
Wagner, 2009
Bahrudin, 2009
Ross, 2001
Nuanain and
O’ Sullivan, 2014
McCallum, 2006
Manec, 2016
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respective parents. The crossover probability is
defined as the probability that two parents will cross
over in single point. The genetic algorithm can have
multi-point crossover versions in which the
crossover rate for a pair of parents is the number of
points at which a crossover takes place*/.
Step 3.3: mutate the two offspring at each
locus with mutation probability pm and transfer the
resulting chromosomes in the new population εTC
(TCNP).
/*If n is odd, then one new population member can
be discarded randomly*/
Step 4: replace the current population with the new
population εRPN (RPNP).
Step 5: has the computational finished (EOC)?
If yes, then go to step 6, otherwise
goto step 2
Step 6: consider parameter εUF and the final
population of bit chromosomes εUF (FPBC)
biological DNA chromosomes, such as strings of
letters from the set {A, C, G, T}. Note that a
particular position (locus) in a chromosome is
referred to as gene, while the letter occurring at that
point in the chromosome is referred to as allele
(value). Each bit-string representation used is
referred to as the genetic algorithm encoding of the
problem. The fitness function is the computed value
for the quality of chromosome as a solution to the
given problem (Engelbrecht, 2002).
A Genetic Algorithm Bio-medical Example
Tumor developments in cancer chemotherapy
can be controlled by delivery of several toxic drugs
subject to various numbers of nonlinear constraints
coupled to combinatorial possibilities for multi-drug
schedules that make difficult the cancer
chemotherapy optimization by using empirical
clinical experimentation or traditional optimization
methods. Various anti-cancer drugs can be delivered
according to discrete dosage program with n-doses
given at time-steps t1, t2, … tn. Every dose includes
d-drugs
with
concentration
levels
Ci , j i 1,..., n and j 1,..., d i=1,…,n, and j=1,…,d of
The simplest algorithmic case of this algorithm
results when εRP = εFC = εPC = εCR = εTC = εRPN = εUF =
1. Note that each iteration of this iterative process is
called a generation.
5. Genetic Algorithms and Evolution Process
anti-cancer drugs in blood plasma. An optimization
of treatment can be achieved by modifying these
variables and then the solution is a set of control
vectors c C i , j representing drug concentration
Genetic algorithms are considered to be
heuristic computational techniques for searching or
optimization, originally motivated by the evolution
principle through genetic- selection process. The
genetic algorithms by using highly abstract versions
of evolutionary processes in order to evolve
solutions to given problems, by operating on certain
populations of artificial chromosomes, i.e. strings in
a finite alphabet (binary). Note that each
chromosome represents a solution of the problem
having a fitness, i.e. a real number measuring how
good the obtained solution is to the given problem.
A random generated population of chromosomes
is selected and a genetic algorithm, with a highly
modular nature, is using a process of fitness-based
selection and recombination in order to produce a
successor population (next generation). This iterated
process produces a sequence of successive
generations and is terminated by a predetermined
stopping criterion, evolving to a good (best) solution
to the original problem.
The genetic algorithms, initially proposed by
Holland (1975), can be efficiently used in a wide
spectrum of scientific, engineering and industrial
problems, particularly in computational intelligence
problems including topics such as, neural networks,
artificial immunology, particle swarm optimization,
ant colony optimization.
Genetic
algorithms
manipulate
several
populations of chromosomes, i.e. abstractions of
profiles.
The response to tumours chemotherapy can be
simulated by the following modified growth model
based on the original growth model of Gompertz
(Gompertz, 1835):
HN
wN (t )
wt
N (t )O ln[ 4N (t )]
¦ dk ¦ nH
j
i 1
C c i , j H H [ H (t t i ) H (t t i 1 )],
(5.1)
i 1
where N(t) represents the number of tumours cells at
time t, H is the Heaviside step function, Ci , j denotes
the concentrations of anti-cancer drugs, kj denote
quantities representing efficacy of anti-cancer drugs
estimated using clinical trial results (20) and Θ, λ are
parameters of tumours growth, and εi are
predetermined adaptive parameters.
Cancer chemotherapy treatment may be (i)
curative attempting to eradicate tumours using
special mechanisms, such as programmed cell death
or immune system, trying to remove remaining
tumour cells, or (ii) palliative applied only when
tumours are deemed to be incurable and having the
objectives of maintaining reasonable quality of life
for patients for as long as possible. Note that the
objective of the two treatments can conflict with
each other in the sense that the minimization of
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tumor sizes are highly toxic and may have negative
effects on patient quality of life.
more sophisticated hybrid algorithms as problemsolving heuristic techniques inspired by the human
immune system in the same way that genetic
algorithms were inspired by evolution.
A known mathematical model for cancer growth
describing tumor growth is given by
N (t ) N o exp( Ot )
(6.1)
While the tumor dynamics can be described by the
so-called Gompertz function or Gompertz model
(Gompertz, 1835), defined as :
N (t ) N o exp{ln[( N f / N 0 )(1 exp( bt )]}
(6.2)
or in parametrized form:
6.
An Algorithmic Treatment using
Genetic Algorithms
Treatment optimization process can be
accomplished by using two genetic algorithms GA-1
and GA-2, where GA-2 is a delta coding genetic
algorithm operating on chromosomes of GA-1. The
genetic algorithms GA-1 and GA-2 can be used for
searching curative or palliative solutions, as
indicated in the following algorithmic procedure.
N (t )
H 0 N o exp{ln[ H N ( N f / N 0 )(1 exp( bt )]}
(6.3)
where N f is the plateau cell number which can be
reached at large values of r and parameter b related
to the initial tumor growth rate. Note that single sets
of growth parameters are insufficient for modelling
clinical data. Tumor cells can have different growth
characteristics in different patients and various
micrometastases within a patient can have different
growth parameters.
Algorithm GA-TREATMENT-1 (εCA1 GA-1, εDCS
GA-2, DCS, FPT, εUF PTC)
Purpose: application of genetic algorithms GA-1 and
GA-2 for patient curative or palliative
solutions PTC
Input: genetic algorithms GA-1 and GA-2, parameter
εDCS, εFPT, and undetermined factor parameter εUF
Output: patient curative or palliative solution εPTC
PTC
Computational Procedure:
Step 0: consider the values of parameters εDCS, εFPT
Step 1: apply εCA1 CA-1 for eradicating tumors
Step 2: check if there is feasible solution.
Step 3: If there is feasible solution then,
apply εDCS GA-2 to delta code solution and
goto
step 6, otherwise
Step 4: apply εFPT GA-1 to fin palliative treatment
Step 5: apply εDCS GA-2 to delta code solution
Step 5a: consider the value of undetermined factor
parameter εUF
Step 6: check the patient treatment and overall
condition εUF PTC
7.
Conclusion
Certain issues of research topics of Artificial
Intelligence and Business have been examined.
Several AI methodologies and their usage in several
organizations in various applications concerning
staffing, careers etc. and hiring predictions were
discussed. The concept of genetic and generic
algorithms with their several important aspects were
also discussed and various related applications in a
wide spectrum of topics have been presented.
Various genetic programming implementations with
the corresponding related software products were
given and several AI software packages, genetic
algorithmic methodologies and related applications
have been given.
Genetic algorithms have been efficiently applied to
many optimal control problems and multi-objective
optimization algorithms of chemotherapy have been
presented (Van der Meulen et al., 2001). Artificial
immune systems can be used for the development of
Benhaddou, D. and Al-Fuqaha, A., 2015. Wireless
Sensor and Mobile Ad-Hoc Networks (p. 253). by Driss
Benhaddou and Ala Al-Fuqaha.Benson T., Akella A. and
Maltz D.A. (2010): Network Traffic Characteristics of
Data Centers in the Wild, IMC-2010.
Ouzounis, C.A., 2012. Rise and demise of
bioinformatics?
Promise
and
progress.
PLoS
computational biology, 8(4), p.e1002487.
Dos Reis, G. and Järvi, J., 2005. What is generic
programming?,
Library-Centric
Software
Design
(LCSD’05), p.1. El
Karoui N., 2013: The Future of Financial Mathematics,
ParisTech Review.
REFERENCES
Dupuis, R., Bourque, P., Abran, A., Moore, J.W. and
Tripp, L.L., 1998, December. Guide to the software
engineering body of knowledge. In 1999 Frontiers in
Education Conference–FIE (Vol. 99, pp. 10-13)
Council, A.C.M., 2000. A summary of the acm position
on software engineering as a licensed engineering
profession.
George, A., Rajakumar, B.R. and Binu, D., 2012,
August. Genetic algorithm based airlines booking terminal
open/close decision system. In proceedings of the
International Conference on Advances in Computing,
Communications and Informatics (pp. 174-179). ACM.
Alsever J., 2017: How A.I. is changing your job hunt and
what the new algorithms want, Fortune 7
294
Authorized licensed use limited to: University of Exeter. Downloaded on June 21,2020 at 18:23:21 UTC from IEEE Xplore. Restrictions apply.
Goldberg, Y., 2016. A primer on neural network models
for natural language processing. Journal of Artificial
Intelligence Research, 57, pp.345-420.
Hogeweg, P., 2011. The roots of bioinformatics in
theoretical biology. PLoS computational biology,7(3), p.
e1002021.
Krawiec, K. and Pawlak, M., 2015, April. Genetic
programming with alternative search drivers for detection
of retinal blood vessels. In European Conference on the
Applications of Evolutionary Computation (pp. 554-566).
Springer, Cham.
Mánek, P., 2016. Genetic programming in Swift for
human-competitive evolution, Univerzita Karlova,
Matematicko-fyzikalni fakulta
Marr B., 2016: The Top 10 AI and Machine Learning
Use
Cases
Everyone
Should
Know
About,
https://www.forbes.com/sites/bernardmarr/2016/09/30/wh
at-are-the-top-10-use-cases-for-machine-learning-andai/#5a4f59cf94c9
McGrath, J., 2016. The rise of AI and algorithms in the
financial services sector, Raconteur EU N, 70.
Morio, J. and Balesdent, M., 2015, Estimation of rare
event probabilities in complex aerospace and other
systems: a practical approach. Woodhead Publishing.
Obradović, D., 2018. Review of nature-inspired
optimization algorithms applied in civil engineering. EGFOS, 9(17), pp.74-88
Perone C.S., 2014: Pyevolve 0.6rc1, Python Software
Foundation, http://pyevolve.sourceforge.net
Schmieder A. (2014): Swift, C++ Performance,
http://www.primatelabs.com/
blog/2014/12/swiftperformance/.
Perone C.S., 2014: Pyevolve 0.6rc1, Python Software
Foundation, http://pyevolve.sourceforge.net
Schmieder A. (2014): Swift, C++ Performance,
http://www.primatelabs.com/
blog/2014/12/swiftperformance/.
Searson, D.P., Leahy, D.E. and Willis, M.J., 2010,
March. GPTIPS: an open source genetic programming
toolbox for multigene symbolic regression. In Proceedings
of the International multiconference of engineers and
computer scientists (Vol. 1, pp. 77-80). Hong Kong:
IMECS.
Spector, L. and Helmuth, T., 2014. Uniform linear
transformation with repair and alternation in genetic
programming. In Genetic Programming Theory and
Practice XI (pp. 137-153). Springer, New York, NY.
Stanislawska, K., Krawiec, K. and Kundzewicz, Z.W.,
2012. Modeling global temperature changes with genetic
programming. Computers & Mathematics with
Applications, 64(12), pp.3717-3728.
Wong, K.C., Peng, C., Wong, M.H. and Leung, K.S.,
2011. Generalizing and learning protein-DNA binding
sequence representations by an evolutionary algorithm.
Soft Computing, 15(8), pp.1631-1642.
Zhang, J., Zhan, Z.H., Lin, Y., Chen, N., Gong, Y.J.,
Zhong, J.H., Chung, H.S., Li, Y. and Shi, Y.H., 2011.
Evolutionary computation meets machine learning: A
survey. IEEE Computational Intelligence Magazine, 6(4),
pp.68-75.
295
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