Author: Community

Beginning with Volume 24, Genetic Programming and Evolvable Machines has adopted a “continuous publication” model, in which articles are published in open issues as soon as their production processes are complete. 

In this context it no longer seems to make sense to post new issue announcements on this blog. 

To see the latest published articles, please check the Volumes and Issues section of the journal’s website.

The GECCO conference offers authors of recent GP+EM papers an opportunity to present it in front of an audience. See https://gecco-2023.sigevo.org/Call-for-HOPs

The fourth issue of Volume 23 of Genetic Programming and Evolvable Machines is now available for download.

This issue includes papers in the Special Issue on Evolutionary Computation in Art, Music and Design.

It contains:

A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit
by Xinming Shi, Leandro L. Minku, and Xin Yao

Using estimation of distribution algorithm for procedural content generation in video games
by Arash Moradi Karkaj and Shahriar Lotfi

Complexity and aesthetics in generative and evolutionary art
by Jon McCormack and Camilo Cruz Gambardella

Experiments in evolutionary image enhancement with ELAINE
by João Correia, Daniel Lopes, Leonardo Vieira, Nereida Rodriguez-Fernandez, Adrian Carballal, Juan Romero and Penousal Machado

BOOK REVIEW
Melanie Mitchell: Artificial intelligence—a guide for thinking humans
by Didem Özkiziltan

BOOK REVIEW
Machado, Romero and Greenfield (editors): Artificial intelligence and the arts
by Anna Jordanous

BOOK REVIEW
The evolution of complexity
by Emily Dolson

BOOK REVIEW
Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification—an automated approach to feature learning
by Amelia Zafra

The third issue of Volume 23 of Genetic Programming and Evolvable Machines is now available for download. 

This is a Special Issue on Highlights of Genetic Programming 2021 Events, edited by Leonardo Trujillo, Nuno Lourenço, Ting Hu, and Mengjie Zhang.

It contains:

Editorial Introduction
by Leonardo Trujillo, Ting Hu, Nuno Lourenço, and Mengjie Zhang

Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set
by Guilherme Seidyo Imai Aldeia and Fabrício Olivetti de França

Evolutionary approximation and neural architecture search
by Michal Pinos, Vojtech Mrazek, and Lukas Sekanina

Applying genetic programming to PSB2: the next generation program synthesis benchmark suite
by Thomas Helmuth and Peter Kelly

Severe damage recovery in evolving soft robots through differentiable programming
by Kazuya Horibe, Kathryn Walker, Rasmus Berg Palm, Shyam Sudhakaran, and Sebastian Risi

A grammar-based GP approach applied to the design of deep neural networks
by Ricardo H. R. Lima, Dimmy Magalhães, Aurora Pozo, Alexander Mendiburu, and Roberto Santana

Presents the table of contents for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Classification data are usually represented by many features, but not all of them are useful. Without domain knowledge, it is challenging to determine which features are useful. Feature selection is an effective preprocessing technique for enhancing the discriminating ability of data, but it is a difficult combinatorial optimization problem because of the challenges of the huge search space and complex interactions between features. Particle swarm optimization (PSO) has been successfully applied to feature selection due to its efficiency and easy implementation. However, most existing PSO-based feature selection methods still face the problem of falling into local optima. To solve this problem, this article proposes a novel PSO-based feature selection approach, which can continuously improve the quality of the population at each iteration. Specifically, a correlation-guided updating strategy based on the characteristic of data is developed, which can effectively use the information of the current population to generate more promising solutions. In addition, a particle selection strategy based on a surrogate technique is presented, which can efficiently select particles with better performance in both convergence and diversity to form a new population. Experimental comparing the proposed approach with a few state-of-the-art feature selection methods on 25 classification problems demonstrate that the proposed approach is able to select a smaller feature subset with higher classification accuracy in most cases.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

We present techniques for improving the efficiency of ND-trees in the solution of the dynamic nondominance problem, i.e., for building and maintaining a Pareto archive. We propose algorithms for (re)building a tree from a set of nondominated points, either as a space-partitioned ND-tree or by a clustering approach. Numerical experiments confirm that rebuilding the tree at intervals, combined with modified strategies for updating the lower and upper bounds in each node and for computing the distances of points to subtrees in between the rebuilds, can lead to significant speedups over using plain ND-tree-based updates throughout, in particular for higher dimensions.

In offline data-driven multiobjective optimization, no new data are available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this article, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-based multiobjective evolutionary algorithms and can, thus, handle a large number of objectives. The proposed approaches were tested on distance-based visualizable test problems and the DTLZ suite. The proposed approaches produced solutions with a greater hypervolume, and a lower root mean squared error compared to generic approaches and a transfer learning approach that do not use uncertainty information.