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)

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

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

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

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

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,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I rehearse two issues around the commentary of Whigham and colleagues. (1) There really are many more reasons than those given as to why natural evolution cannot reasonably find or select the 'optimal' individual. (2) A series of experimental molecular biology programmes, known generically as directed evolution, can use operators and selection schemes that natural evolution cannot. When developed further

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

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

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

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

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

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

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

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.

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.