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An enhanced Huffman-PSO based image optimization algorithm for image steganography Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2021-01-01 Neha Sharma, Usha Batra
It is crucial in the field of image steganography to find an algorithm for hiding information by using various combinations of compression techniques. The primary factors in this research are maximizing the capacity and improving the quality of the image. The image quality cannot be compromised up to a certain level as it breaks the concept of steganography by getting distorted visibly. The second
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Discovering novel memory cell designs for sentiment analysis on tweets Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-11-17 Sergiu Cosmin Nistor, Mircea Moca, Răzvan Liviu Nistor
Designing a Recurrent Neural Network to extract sentiment from tweets is a very hard task. When using memory cells in their design, the task becomes even harder due to the large number of design alternatives and the costly process of finding a performant design. In this paper we propose an original evolutionary algorithm to address the hard challenge of discovering novel Recurrent Neural Network memory
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Fuzzy cognitive maps for decision-making in dynamic environments Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-05-27 Tomas Nachazel
This paper describes a new modification of fuzzy cognitive maps (FCMs) for the modeling of autonomous entities that make decisions in a dynamic environment. The paper offers a general design for an FCM adjusted for the decision-making of autonomous agents through the categorization of its concepts into three different classes according to their purpose in the map: Needs, Activities, and States (FCM-NAS)
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Choosing function sets with better generalisation performance for symbolic regression models Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-05-12 Miguel Nicolau, Alexandros Agapitos
Supervised learning by means of Genetic Programming (GP) aims at the evolutionary synthesis of a model that achieves a balance between approximating the target function on the training data and generalising on new data. The model space searched by the Evolutionary Algorithm is populated by compositions of primitive functions defined in a function set. Since the target function is unknown, the choice
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Stock selection heuristics for performing frequent intraday trading with genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-04-30 Alexander Loginov, Malcolm Heywood, Garnett Wilson
Intraday trading attempts to obtain a profit from the microstructure implicit in price data. Intraday trading implies many more transactions per stock compared to long term buy-and-hold strategies. As a consequence, transaction costs will have a more significant impact on the profitability. Furthermore, the application of existing long term portfolio selection algorithms for intraday trading cannot
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Benchmarking state-of-the-art symbolic regression algorithms Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-03-24 Jan Žegklitz, Petr Pošík
Symbolic regression (SR) is a powerful method for building predictive models from data without assuming any model structure. Traditionally, genetic programming (GP) was used as the SR engine. However, for these purely evolutionary methods it was quite hard to even accommodate the function to the range of the data and the training was consequently inefficient and slow. Recently, several SR algorithms
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Towards the use of genetic programming in the ecological modelling of mosquito population dynamics Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-01-03 Irene Azzali, Leonardo Vanneschi, Andrea Mosca, Luigi Bertolotti, Mario Giacobini
Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering
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Adversarial genetic programming for cyber security: a rising application domain where GP matters Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-04-02 Una-May O’Reilly; Jamal Toutouh; Marcos Pertierra; Daniel Prado Sanchez; Dennis Garcia; Anthony Erb Luogo; Jonathan Kelly; Erik Hemberg
Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security encompasses extant and immediate research efforts in a
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Learning feature spaces for regression with genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-03-11 William La Cava; Jason H. Moore
Genetic programming has found recent success as a tool for learning sets of features for regression and classification. Multidimensional genetic programming is a useful variant of genetic programming for this task because it represents candidate solutions as sets of programs. These sets of programs expose additional information that can be exploited for building block identification. In this work,
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Genetic programming in the steelmaking industry Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-02-07 Miha Kovačič; Uroš Župerl
Genetic programming is a powerful, robust and versatile tool that is suitable for predicting and forecasting, especially in the steelmaking industry, where the diversity of serial production processes and equipment strongly influence final product properties, quality and price. The article reviews a wide spectrum of implementation attempts of genetic programing in the steelmaking industry, including
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Evolutionary music: applying evolutionary computation to the art of creating music Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-02-06 Róisín Loughran; Michael O’Neill
We present a review of the application of genetic programming (GP) and other variations of evolutionary computation (EC) to the creative art of music composition. Throughout the development of EC methods, since the early 1990s, a small number of researchers have considered aesthetic problems such as the act of composing music alongside other more traditional problem domains. Over the years, interest
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Multi-objective genetic programming for manifold learning: balancing quality and dimensionality Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-02-05 Andrew Lensen; Mengjie Zhang; Bing Xue
Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they
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Horizontal gene transfer for recombining graphs Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-02-03 Timothy Atkinson; Detlef Plump; Susan Stepney
We introduce a form of neutral horizontal gene transfer (HGT) to evolving graphs by graph programming (EGGP). We introduce the \(\mu \times \lambda\) evolutionary algorithm (EA), where \(\mu\) parents each produce \(\lambda\) children who compete only with their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging
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EA-based resynthesis: an efficient tool for optimization of digital circuits Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-01-30 Jitka Kocnova; Zdenek Vasicek
Since the early nineties the lack of scalability of fitness evaluation has been the main bottleneck preventing the adoption of evolutionary algorithms for logic circuits synthesis. Recently, various formal approaches such as SAT and BDD solvers have been introduced to this field to overcome this issue. This made it possible to optimise complex circuits consisting of hundreds of inputs and thousands
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On the importance of specialists for lexicase selection Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-01-30 Thomas Helmuth; Edward Pantridge; Lee Spector
Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individual with an error for the current case that is worse than the best error of any individual in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were
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A network perspective on genotype–phenotype mapping in genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2020-01-29 Ting Hu; Marco Tomassini; Wolfgang Banzhaf
Genotype–phenotype mapping plays an essential role in the design of an evolutionary algorithm. Variation occurs at the genotypic level but fitness is evaluated at the phenotypic level, therefore, this mapping determines if and how variations are effectively translated into quality improvements. In evolutionary algorithms, this mapping has often been observed as highly redundant, i.e., multiple genotypes
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Unimodal optimization using a genetic-programming-based method with periodic boundary conditions Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-12-17 Rogério C. B. L. Póvoa; Adriano S. Koshiyama; Douglas M. Dias; Patrícia L. Souza; Bruno A. C. Horta
This article describes a new genetic-programming-based optimization method using a multi-gene approach along with a niching strategy and periodic domain constraints. The method is referred to as Niching MG-PMA, where MG refers to multi-gene and PMA to parameter mapping approach. Although it was designed to be a multimodal optimization method, recent tests have revealed its suitability for unimodal
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Parameter identification for symbolic regression using nonlinear least squares Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-12-10 Michael Kommenda; Bogdan Burlacu; Gabriel Kronberger; Michael Affenzeller
In this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local search mechanism in tree-based genetic programming. We employ the Levenberg–Marquardt algorithm for parameter optimization and calculate gradients via automatic differentiation. We provide examples where the parameter identification succeeds and
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GPML: an XML-based standard for the interchange of genetic programming trees Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-11-27 Tiantian Dou, Yuri Kaszubowski Lopes, Peter Rockett, Elizabeth A. Hathway, Esmail Saber
We propose a genetic programming markup language (GPML), an XML-based standard for the interchange of genetic programming trees, and outline the benefits such a format would bring in allowing the deployment of trained genetic programming (GP) models in applications as well as the subsidiary benefit of allowing GP researchers to directly share trained trees. We present a formal definition of this standard
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Evolutionary design model of passive filter circuit for practical application Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-11-19 Jingsong He, Jin Yin
Evolutionary circuit design is a promising way to study new circuit design methodologies, and the passive filter is the most basic circuit module widely existing in modern electronic systems. Focused on the basic and fatal criterion related to the filter circuit design, this paper presents a novel evolutionary design model of passive filter circuit. The proposed model includes a circuit representation
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Transfer learning in constructive induction with Genetic Programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-11-05 Luis Muñoz, Leonardo Trujillo, Sara Silva
Transfer learning (TL) is the process by which some aspects of a machine learning model generated on a source task is transferred to a target task, to simplify the learning required to solve the target. TL in Genetic Programming (GP) has not received much attention, since it is normally assumed that an evolved symbolic expression is specifically tailored to a problem’s data and thus cannot be used
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Automated discovery of test statistics using genetic programming. Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-05-21 Jason H Moore,Randal S Olson,Yong Chen,Moshe Sipper
The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about
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Software review: the GPTIPS platform Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-10-29 Amir H. Gandomi; Ehsan Atefi
GPTIPS is a widely used genetic programming software that was developed in Matlab. The most recent version of this software, GPTIPS 2.0, provides a symbolic multi-gene regression for data analysis, in addition to traditional evolutionary algorithms. We briefly explain the GPTIPS methodology and describe its main features, including its weaknesses and strengths, and give examples of GPTIPS applications
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Automatic programming: The open issue? Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-09-11 Michael O’Neill; Lee Spector
Automatic programming, the automatic generation of a computer program given a high-level statement of the program’s desired behaviour, is a stated objective of the field of genetic programming. As the general solution to a computational problem is to write a computer program, and given that genetic programming can automatically generate a computer program, researchers in the field of genetic programming
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Applications of genetic programming to finance and economics: past, present, future Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-08-08 Anthony Brabazon; Michael Kampouridis; Michael O’Neill
While the origins of genetic programming (GP) stretch back over 50 years, the field of GP was invigorated by John Koza’s popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics
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Cartesian genetic programming: its status and future Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-08-06 Julian Francis Miller
Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are well-known to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed
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Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-07-27 Andrea De Lorenzo; Alberto Bartoli; Mauro Castelli; Eric Medvet; Bing Xue
In this work we present an extensive bibliometric and content-based analysis of the scientific literature about genetic programming in the twenty-first century. Our work has two key peculiarities. First, we revealed the topics emerging from the literature based on an unsupervised analysis of the textual content of titles and abstracts. Second, we executed all of our analyses twice, once on the papers
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The impact of genetic programming in education Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-07-26 Nelishia Pillay
Since its inception genetic programming, and later variations such as grammar-based genetic programming and grammatical evolution, have contributed to various domains such as classification, image processing, search-based software engineering, amongst others. This paper examines the role that genetic programming has played in education. The paper firstly provides an overview of the impact that genetic
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Genetic programming for natural language processing Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-07-23 Lourdes Araujo
This work takes us through the literature on applications of genetic programming to problems of natural language processing. The purpose of natural language processing is to allow us to communicate with computers in natural language. Among the problems addressed in the area is, for example, the extraction of information, which draws relevant data from unstructured texts written in natural language
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A journey among Java neutral program variants Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-06-25 Nicolas Harrand; Simon Allier; Marcelino Rodriguez-Cancio; Martin Monperrus; Benoit Baudry
Neutral program variants are alternative implementations of a program, yet equivalent with respect to the test suite. Techniques such as approximate computing or genetic improvement share the intuition that potential for enhancements lies in these acceptable behavioral differences (e.g., enhanced performance or reliability). Yet, the automatic synthesis of neutral program variants, through program
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A novel multi-swarm particle swarm optimization for feature selection Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-06-20 Chenye Qiu
A novel feature selection method based on a multi-swarm particle swarm optimization (MSPSO) is proposed in this paper. The canonical particle swarm optimization (PSO) has been widely used for feature selection problems. However, PSO suffers from stagnation in local optimal solutions and premature convergence in complex feature selection problems. This paper employs the multi-swarm topology in which
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A covariance matrix adaptation evolution strategy in reproducing kernel Hilbert space Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-06-19 Viet-Hung Dang; Ngo Anh Vien; TaeChoong Chung
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well-defined parameter space in which feature functions are often defined manually. Therefore, the performance of those techniques strongly depends on the quality of the chosen features or the underlying parametric function space. Hence
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A survey of evolutionary algorithms using metameric representations Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-06-17 Matt Ryerkerk; Ron Averill; Kalyanmoy Deb; Erik Goodman
Evolutionary algorithms have been used to solve a number of variable-length problems, many of which share a common representation. A set of design variables is repeatedly defined, giving the genome a segmented structure. Each segment encodes a portion, frequently a single component, of the solution. For example, in a wind farm design problem each segment may encode the position and height of a single
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Evolving autoencoding structures through genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-05-25 Lino Rodriguez-Coayahuitl; Alicia Morales-Reyes; Hugo Jair Escalante
We propose a novel method to evolve autoencoding structures through genetic programming (GP) for representation learning on high dimensional data. It involves a partitioning scheme of high dimensional input representations for distributed processing as well as an on-line form of learning that allows GP to efficiently process training datasets composed of hundreds or thousands of samples. The use of
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Genetic programming theory and practice: a fifteen-year trajectory Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-05-16 Moshe Sipper; Jason H. Moore
The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of GP over the past fifteen years. We thus present herein a trajectory of the thematic developments in the field of GP.
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GP-based methods for domain adaptation: using brain decoding across subjects as a test-case Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-05-10 Roberto Santana; Luis Marti; Mengjie Zhang
Research on classifier transferability intends that the information gathered in the solution of a given classification problem could be reused in the solution of similar or related problems. We propose the evolution of transferable classifiers based on the use of multi-objective genetic programming and new fitness-functions that evaluate the amount of transferability. We focus on the domain adaptation
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Local search in speciation-based bloat control for genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-03-23 Perla Juárez-Smith; Leonardo Trujillo; Mario García-Valdez; Francisco Fernández de Vega; Francisco Chávez
This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism to address some of the main issues with traditional GP. The former provides a directed search operator to work in conjunction with standard syntax operators that perform more exploration in design space, while the latter controls code growth by maintaining program
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Stochastic synthesis of recursive functions made easy with bananas, lenses, envelopes and barbed wire Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-03-14 Jerry Swan; Krzysztof Krawiec; Zoltan A Kocsis
Stochastic synthesis of recursive functions has historically proved difficult, not least due to issues of non-termination and the often ad hoc methods for addressing this. This article presents a general method of implicit recursion which operates via an automatically-derivable decomposition of datatype structure by cases, thereby ensuring well-foundedness. The method is applied to recursive functions
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A genetic programming framework in the automatic design of combination models for salient object detection Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-03-05 Marco A. Contreras-Cruz; Diana E. Martinez-Rodriguez; Uriel H. Hernandez-Belmonte; Victor Ayala-Ramirez
In computer vision, the salient object detection problem consists of finding the most attention-grabbing objects in images. In the last years, many researchers have proposed salient object detection algorithms to address this problem. However, most of the algorithms perform well only on images with specific conditions and they do not solve the general problem. To cope with a more significant number
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A multi-level grammar approach to grammar-guided genetic programming: the case of scheduling in heterogeneous networks Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-03-01 Takfarinas Saber; David Fagan; David Lynch; Stepan Kucera; Holger Claussen; Michael O’Neill
The scale at which the human race consumes data has increased exponentially in recent years. One key part in this increase has been the usage of smart phones and connected devices by the populous. Multi-level heterogeneous networks are the driving force behind this mobile revolution, but these are constrained with limited bandwidth and over-subscription. Scheduling users on these networks has become
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Genetic programming and evolvable machines at 20 Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-02-27 W. B. Langdon
The journal and in particular the resource reviews have been running for 20 years. We summarise the GP literature, including top papers and authors, as seen by users of the genetic programming bibliography. Then revisit our original goals for GPEM book reviews and compare them with what has achieved.
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EvoParsons: design, implementation and preliminary evaluation of evolutionary Parsons puzzle Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-02-05 A. T. M. Golam Bari; Alessio Gaspar; R. Paul Wiegand; Jennifer L. Albert; Anthony Bucci; Amruth N. Kumar
The automated design of a set of practice problems that co-adapts to a population of learners is a challenging problem. Fortunately, coevolutionary computation offers a rich framework to study interactions between two co-adapting populations of teachers and learners. This framework is also relevant in scenarios in which a population of students solve practice exercises that are synthesized by an evolutionary
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Vector quantization using the improved differential evolution algorithm for image compression Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2019-01-16 Sayan Nag
Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PSNR value. A codebook affects the quality of image compression, so the choice of an appropriate codebook
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Software review: DEAP (Distributed Evolutionary Algorithm in Python) library Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-11-21 Jinhan Kim; Shin Yoo
We give a critical assessment of the DEAP (Distributed Evolutionary Algorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionary algorithms including both strongly and loosely typed Genetic Programming, Genetic Algorithm, and Multi-Objective Evolutionary Algorithms such as NSGA-II and SPEA2. It contains most of the basic
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Automated discovery of test statistics using genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-10-10 Jason H. Moore; Randal S. Olson; Yong Chen; Moshe Sipper
The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about
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On the scalability of evolvable hardware architectures: comparison of systolic array and Cartesian genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-10-01 Javier Mora; Rubén Salvador; Eduardo de la Torre
Evolvable hardware allows the generation of circuits that are adapted to specific problems by using an evolutionary algorithm (EA). Dynamic partial reconfiguration of FPGA LUTs allows making the processing elements (PEs) of these circuits small and compact, thus allowing large scale circuits to be implemented in a small FPGA area. This facilitates the use of these techniques in embedded systems with
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DENSER: deep evolutionary network structured representation Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-09-27 Filipe Assunção; Nuno Lourenço; Penousal Machado; Bernardete Ribeiro
Deep evolutionary network structured representation (DENSER) is a novel evolutionary approach for the automatic generation of deep neural networks (DNNs) which combines the principles of genetic algorithms (GAs) with those of dynamic structured grammatical evolution (DSGE). The GA-level encodes the macro structure of evolution, i.e., the layers, learning, and/or data augmentation methods (among others);
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Evolving continuous cellular automata for aesthetic objectives Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-08-27 Jeff Heaton
We present MergeLife, a genetic algorithm (GA) capable of evolving continuous cellular automata (CA) that generate full color dynamic animations according to aesthetic user specifications. A simple 16-byte update rule is introduced that is evolved through an objective function that requires only initial human aesthetic guidelines. This update rule provides a fixed-length genome that can be successfully
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A genetic programming approach for delta hedging Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-08-18 Zheng Yin; Anthony Brabazon; Conall O’Sullivan; Philip A. Hamill
In this paper, using high-frequency intra-daily data from the UK market, we employ genetic programming (GP) to uncover a hedging strategy for FTSE 100 call options, hedged using FTSE 100 futures contracts. The output from the evolved strategies is a rebalancing signal which is conditioned upon a range of dynamic non-linear factors related to market conditions including liquidity and volatility. When
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Visualisation with treemaps and sunbursts in many-objective optimisation. Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-08-07 David J Walker
Visualisation is an important aspect of evolutionary computation, enabling practitioners to explore the operation of their algorithms in an intuitive way and providing a better means for displaying their results to problem owners. The presentation of the complex data arising in many-objective evolutionary algorithms remains a challenge, and this work examines the use of treemaps and sunbursts for visualising
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Visualising the global structure of search landscapes: genetic improvement as a case study. Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-08-06 Nadarajen Veerapen,Gabriela Ochoa
The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation
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VALIS: an evolutionary classification algorithm Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-08-03 Peter Karpov; Giovanni Squillero; Alberto Tonda
VALIS is an effective and robust classification algorithm with a focus on understandability. Its name stems from Vote-ALlocating Immune System, as it evolves a population of artificial antibodies that can bind to the input data, and performs classification through a voting process. In the beginning of the training, VALIS generates a set of random candidate antibodies; at each iteration, it selects
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Data exploration in evolutionary reconstruction of PET images Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-08-01 Cameron C. Gray; Shatha F. Al-Maliki; Franck P. Vidal
This work is based on a cooperative co-evolution algorithm called ‘Fly Algorithm’, which is an evolutionary algorithm (EA) where individuals are called ‘flies’. It is a specific case of the ‘Parisian Approach’ where the solution of an optimisation problem is a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical EAs. The optimisation problem considered
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Unveiling evolutionary algorithm representation with DU maps Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-07-31 Eric Medvet; Marco Virgolin; Mauro Castelli; Peter A. N. Bosman; Ivo Gonçalves; Tea Tušar
Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly
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Designing automatically a representation for grammatical evolution Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-07-12 Eric Medvet; Alberto Bartoli; Andrea De Lorenzo; Fabiano Tarlao
A long-standing problem in evolutionary computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate
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Comparison of semantic-based local search methods for multiobjective genetic programming Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-07-05 Tiantian Dou; Peter Rockett
We report a series of experiments that use semantic-based local search within a multiobjective genetic programming (GP) framework. We compare various ways of selecting target subtrees for local search as well as different methods for performing that search; we have also made comparison with the random desired operator of Pawlak et al. using statistical hypothesis testing. We find that a standard steady
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Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-07-04 Azam Shirali; Javidan Kazemi Kordestani; Mohammad Reza Meybodi
This paper presents a self-adaptive multi-population approach based on genetic algorithm (GA) for solving dynamic resource allocation in shared hosting platforms. The proposed method, self-adaptive multi-population genetic algorithm (SAMPGA), is a multi-population GA strategy aimed at locating and tracking optima. This approach is based on preventing populations from searching in the same areas. Two
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Grammatical evolution as a hyper-heuristic to evolve deterministic real-valued optimization algorithms Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-06-22 Iztok Fajfar; Árpád Bűrmen; Janez Puhan
Hyper-heuristic methodologies have been extensively and successfully used to generate combinatorial optimization heuristics. On the other hand, there have been almost no attempts to build a hyper-heuristic to evolve an algorithm for solving real-valued optimization problems. In our previous research, we succeeded to evolve a Nelder–Mead-like real function minimization heuristic using genetic programming
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Implementing the template method pattern in genetic programming for improved time series prediction Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2018-03-05 David Moskowitz
Modularity is an ongoing focus in genetic programming research. Enhanced modularity can accelerate solution convergence and increase human understanding and knowledge gained from evolved programs. Prior advances in modularity research have addressed programming language elements such as functions, modules, and recursion. This paper proposes improving modularity by considering non-language elements
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Evolution of shared grammars for describing simulated spatial scenes with grammatical evolution Genet. Program. Evolvable Mach. (IF 1.781) Pub Date : 2017-10-31 Jack Mario Mingo; Ricardo Aler
We propose a model based on an evolutionary process combined with an adapted planning process to develop a limited spatial language with a syntactical structure in a team of artificial agents. Syntax is induced by means of a grammar and the grammar itself evolves in order to reach a syntactical agreement in the team. Evolution is implemented by adapting an evolutionary algorithm where each agent in