样式: 排序: IF: - GO 导出 标记为已读
-
A genetic programming approach to the automated design of CNN models for image classification and video shorts creation Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2024-03-14
Abstract Neural architecture search (NAS) is a rapidly growing field which focuses on the automated design of neural network architectures. Genetic algorithms (GAs) have been predominantly used for evolving neural network architectures. Genetic programming (GP), a variation of GAs that work in the program space rather than a solution space, has not been as well researched for NAS. This paper aims to
-
An ensemble learning interpretation of geometric semantic genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2024-03-11 Grant Dick
-
Neural network crossover in genetic algorithms using genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2024-02-21 Kyle Pretorius, Nelishia Pillay
-
Cellular geometric semantic genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2024-02-21 Lorenzo Bonin, Luigi Rovito, Andrea De Lorenzo, Luca Manzoni
-
Geometric semantic genetic programming with normalized and standardized random programs Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2024-02-08 Illya Bakurov, José Manuel Muñoz Contreras, Mauro Castelli, Nuno Rodrigues, Sara Silva, Leonardo Trujillo, Leonardo Vanneschi
-
Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic scheduling Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2024-01-25 Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang, Wolfgang Banzhaf
-
Creative collaboration with interactive evolutionary algorithms: a reflective exploratory design study Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-12-18 Severi Uusitalo, Anna Kantosalo, Antti Salovaara, Tapio Takala, Christian Guckelsberger
-
Semantic mutation operator for a fast and efficient design of bent Boolean functions Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-12-08 Jakub Husa, Lukáš Sekanina
-
A geometric semantic macro-crossover operator for evolutionary feature construction in regression Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-12-08 Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf, Mengjie Zhang
-
-
W. B. Langdon “Jaws 30” Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-11-22 Malcolm I. Heywood
At the 30th anniversary of ‘Jaws’, the Genetic programming field has much to celebrate. However, in order continue to build on these successes, it might be necessary to look more deeply into the “less successful” and/or “less explored” topics. We consider the role of FPGA and GPU platforms from the former and coevolution from the latter.
-
Response to comments on “Jaws 30” Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-11-22 W. B. Langdon
-
Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-11-08 David Wittenberg, Franz Rothlauf, Christian Gagné
-
On the hybridization of geometric semantic GP with gradient-based optimizers Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-10-28 Gloria Pietropolli, Luca Manzoni, Alessia Paoletti, Mauro Castelli
-
Semantic segmentation network stacking with genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-10-26 Illya Bakurov, Marco Buzzelli, Raimondo Schettini, Mauro Castelli, Leonardo Vanneschi
-
A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-10-24 Mohammad Hassan Tayarani Najaran
-
Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-10-20 Fabrício Olivetti de França
-
Reward tampering and evolutionary computation: a study of concrete AI-safety problems using evolutionary algorithms Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-09-19 Mathias K. Nilsen, Tønnes F. Nygaard, Kai Olav Ellefsen
-
Evolutionary design and analysis of ribozyme-based logic gates Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-09-05 Nicolas Kamel, Nawwaf Kharma, Jonathan Perreault
-
GAAMmf: genetic algorithm with aggressive mutation and decreasing feature set for feature selection Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-07-26 Rejer Izabela, Lorenz Krzysztof
-
Evolutionary design of swing-up controllers for stabilization task of underactuated inverted pendulums Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-07-22 Marlen Meza-Sánchez, M. C. Rodríguez-Liñán, Eddie Clemente, Leonardo Herrera
-
Evolutionary combination of connected event schemas into meaningful plots Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-06-07 Pablo Gervás, Gonzalo Méndez, Eugenio Concepción
-
Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisation Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-05-17 Jordan J. Bird, Ahmad Lotfi
-
A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-03-28 Fraser Borrett, Mark Beckerleg
-
Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-03-24 Matthew Andres Moreno, Alexander Lalejini, Charles Ofria
-
SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-03-13 Tasos Asonitis, Richard Allmendinger, Matt Benatan, Ricardo Climent
-
Framework for unsupervised incremental evolution of stylized images Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-02-28 Florian Uhde
-
A new hybrid method of Evolutionary-Numerical algorithms to solve ODEs arising in physics and engineering Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2023-02-14 S. R. Mirshafaei, H. Saberi Najafi, E. khaleghi, A. H. Refahi Sheikhani
-
Benchmarking ensemble genetic programming with a linked list external memory on scalable partially observable tasks Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-11-30 Mihyar Al Masalma, Malcolm Heywood
-
Experiments in evolutionary image enhancement with ELAINE Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-11-01 João Correia, Daniel Lopes, Leonardo Vieira, Nereida Rodriguez-Fernandez, Adrian Carballal, Juan Romero, Penousal Machado
-
Using estimation of distribution algorithm for procedural content generation in video games Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-08-02 Arash Moradi Karkaj, Shahriar Lotfi
-
A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-07-21 Xinming Shi, Leandro L. Minku, Xin Yao
-
Evolutionary approximation and neural architecture search Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-06-11 Michal Pinos, Vojtech Mrazek, Lukas Sekanina
-
Severe damage recovery in evolving soft robots through differentiable programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-06-12 Kazuya Horibe, Kathryn Walker, Rasmus Berg Palm, Shyam Sudhakaran, Sebastian Risi
-
A grammar-based GP approach applied to the design of deep neural networks Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-06-02 Ricardo H. R. Lima, Dimmy Magalhães, Aurora Pozo, Alexander Mendiburu, Roberto Santana
-
Applying genetic programming to PSB2: the next generation program synthesis benchmark suite Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-06-01 Thomas Helmuth, Peter Kelly
-
Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-05-30 Guilherme Seidyo Imai Aldeia, Fabrício Olivetti de França
-
Complexity and aesthetics in generative and evolutionary art Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-04-26 Jon McCormack, Camilo Cruz Gambardella
-
On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-04-09 Ciniro A. L. Nametala, Wandry R. Faria, Benvindo R. Pereira Júnior
-
-
GP-DMD: a genetic programming variant with dynamic management of diversity Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-01-21 Ricardo Nieto-Fuentes, Carlos Segura
The proper management of diversity is essential to the success of Evolutionary Algorithms. Specifically, methods that explicitly relate the amount of diversity maintained in the population to the stopping criterion and elapsed period of execution, with the aim of attaining a gradual shift from exploration to exploitation, have been particularly successful. However, in the area of Genetic Programming
-
Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2022-01-16 Daniel Varela, José Santos
Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through
-
Genetic programming for iterative numerical methods Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-11-25 Dominik Sobania, Jonas Schmitt, Harald Köstler, Franz Rothlauf
We introduce GPLS (Genetic Programming for Linear Systems) as a GP system that finds mathematical expressions defining an iteration matrix. Stationary iterative methods use this iteration matrix to solve a system of linear equations numerically. GPLS aims at finding iteration matrices with a low spectral radius and a high sparsity, since these properties ensure a fast error reduction of the numerical
-
Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-11-18 Sergio Contador, J. Manuel Colmenar, Oscar Garnica, J. Manuel Velasco, J. Ignacio Hidalgo
In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the
-
Artificial intelligence for fashion, Leanne Luce, Apress 2019, ISBN 978-1-4842-3930-8 how AI is revolutionizing the fashion industry Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-26 Grace Buttler
-
Robert Elliott Smith: Rage Inside the Machine—the prejudice of algorithms, and how to stop the internet making bigots of us all Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-25 Walid Magdy
-
Generating networks of genetic processors Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-21 Campos, Marcelino, Sempere, José M.
The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines
-
Evolving continuous optimisers from scratch Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-20 Lones, Michael A.
This work uses genetic programming to explore the space of continuous optimisers, with the goal of discovering novel ways of doing optimisation. In order to keep the search space broad, the optimisers are evolved from scratch using Push, a Turing-complete, general-purpose, language. The resulting optimisers are found to be diverse, and explore their optimisation landscapes using a variety of interesting
-
Highlights of genetic programming 2020 events. Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-16 Miguel Nicolau
-
Evolving hierarchical memory-prediction machines in multi-task reinforcement learning Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-09 Kelly, Stephen, Voegerl, Tatiana, Banzhaf, Wolfgang, Gondro, Cedric
A fundamental aspect of intelligent agent behaviour is the ability to encode salient features of experience in memory and use these memories, in combination with current sensory information, to predict the best action for each situation such that long-term objectives are maximized. The world is highly dynamic, and behavioural agents must generalize across a variety of environments and objectives over
-
A semantic genetic programming framework based on dynamic targets Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-05 Ruberto, Stefano, Terragni, Valerio, Moore, Jason H.
Semantic GP is a promising branch of GP that introduces semantic awareness during genetic evolution to improve various aspects of GP. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors of previous runs. To obtain the final solution
-
Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-02 Hodan, David, Mrazek, Vojtech, Vasicek, Zdenek
Cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5\(\times\)5-bit multiplier
-
Automatic generation of regular expressions for the Regex Golf challenge using a local search algorithm Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-10-01 de Almeida Farzat, André, de Oliveira Barros, Márcio
Regular expression is a technology widely used in software development for extracting textual data, validating the structure of textual documents, or formatting data. Regex Golf is a challenge that consists in finding the smallest possible regular expression given a set of sentences to perform matches and another set not to match. An algorithm capable of meeting the Regex Golf requirements is a relevant
-
Graph representations in genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-09-30 Françoso Dal Piccol Sotto, Léo, Kaufmann, Paul, Atkinson, Timothy, Kalkreuth, Roman, Porto Basgalupp, Márcio
Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences
-
Relationships between parent selection methods, looping constructs, and success rate in genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-09-30 Saini, Anil Kumar, Spector, Lee
In genetic programming, parent selection methods are employed to select promising candidate individuals from the current generation that can be used as parents for the next generation. These algorithms can affect, sometimes indirectly, whether or not individuals containing certain programming constructs, such as loops, are selected and propagated in the population. This in turn can affect the chances
-
Evolutionary algorithms for designing reversible cellular automata Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-09-29 Mariot, Luca, Picek, Stjepan, Jakobovic, Domagoj, Leporati, Alberto
Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by dynamics composed only of disjoint cycles. They have many applications in the simulation of physical systems, cryptography, and reversible computing. In this work, we formulate the search of a specific class of RCA – namely, those whose local update rules are defined by conserved landscapes
-
EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-09-03 Schmitt, Jonas, Kuckuk, Sebastian, Köstler, Harald
For many systems of linear equations that arise from the discretization of partial differential equations, the construction of an efficient multigrid solver is challenging. Here we present EvoStencils, a novel approach for optimizing geometric multigrid methods with grammar-guided genetic programming, a stochastic program optimization technique inspired by the principle of natural evolution. A multigrid
-
Genetic programming convergence Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-08-30 Langdon, W. B.
We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows
-
Constant optimization and feature standardization in multiobjective genetic programming Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-08-19 Rockett, Peter
This paper extends the numerical tuning of tree constants in genetic programming (GP) to the multiobjective domain. Using ten real-world benchmark regression datasets and employing Bayesian comparison procedures, we first consider the effects of feature standardization (without constant tuning) and conclude that standardization generally produces lower test errors, but, contrary to other recently published
-
Inference of time series components by online co-evolution Genet. Program. Evolvable Mach. (IF 2.6) Pub Date : 2021-07-21 Danil Koryakin, Sebastian Otte, Martin V. Butz
Time series data is often composed of a multitude of individual, superimposed dynamics. We propose a novel algorithm for inferring time series compositions through evolutionary synchronization of modular networks (ESMoN). ESMoN orchestrates a set of trained dynamic modules, assuming that some of those modules’ dynamics, suitably parameterized, will be present in the targeted time series. With the help