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Natural Computing OnlineFirst articles


On the right combination of altruism and randomness in the motion of homogeneous distributed autonomous agents

We demonstrate the role of randomness and altruism in the motion of artificial agents in a deterministic environment. A swarm of distributed autonomous agents with no possibility of coordination tracks a unique target. The goal is to reach the …

Michael Hassoun, Evgeny Kagan


Verification and computation in restricted Tile Automata

Many models of self-assembly have been shown to be capable of performing computation. Tile Automata was recently introduced combining features of both Cellular Automata and the 2-Handed Model of self-assembly both capable of universal computation.

David Caballero, Timothy Gomez, Robert Schweller, Tim Wylie


Population-induced phase transitions and the verification of chemical reaction networks

We show that very simple molecular systems, modeled as chemical reaction networks, can have behaviors that exhibit dramatic phase transitions at certain population thresholds. Moreover, the magnitudes of these thresholds can thwart attempts to use …

James I. Lathrop, Jack H. Lutz, Robyn R. Lutz, Hugh D. Potter, Matthew R. Riley


On the predictability of the abelian sandpile model

We study two questions related to the abelian sandpile model, those questions are: can we predict the dynamics of sandpiles avalanches? Can we efficiently stop an evolving avalanche? We study the problem of deciding wether all the nodes of a …

J. Andres Montoya, Carolina Mejia



Alonso Castillo-Ramirez, Pedro Paulo Balbi de Oliveira


Watson–Crick automata languages-without sensing parameter

Watson–Crick (WK) finite automata are working on a Watson–Crick tape, that is, on an abstract construct similar to DNA molecules. Therefore, it has two reading heads. While in traditional WK automata both heads read the whole input in the same …

Benedek Nagy, Shaghayegh Parchami



Special issue on “Understanding of Evolutionary Optimization Behavior”, Part 2

The history of Evolutionary Computation has progressed from formal studies to a method-centric and framework-centric period, where many algorithms are described as methods or frameworks and their development is primarily performance-driven. We are …

Christian Blum, Tome Eftimov, Peter Korošec

13.06.2021 Open Access

Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax

Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains …

Moritz Mühlenthaler, Alexander Raß, Manuel Schmitt, Rolf Wanka



José Manuel Ferrández Vicente, José Santos Reyes


Enhancing differential evolution algorithm through a population size adaptation strategy

As one of the three basic control parameters of the differential evolution algorithm (DE), the population size (PS) has attracted extensive attention. In general, the most appropriate population size varies for different types of problems and …

Yanyun Zhang, Guangming Dai, Lei Peng, Maocai Wang


Blind methods to build choice-based ensembles

This paper aims at developing new models to combine the best of two paradigms: the performance of ensembles and the transparency of choice models. Specifically, the work explores several blind methods to build ensembles of single choice-based …

Ameed Almomani, Eduardo Sánchez


The effect of jumping modes on various automata models

Recently, new types of non-sequential machine models have been introduced and studied, such as jumping automata and one-way jumping automata. We study the abilities and limitations of (finite, pushdown and linear bounded) automata with these 2 …

Szilárd Zsolt Fazekas, Kaito Hoshi, Akihiro Yamamura

20.02.2021 Open Access

Understanding measure-driven algorithms solving irreversibly ill-conditioned problems

The paper helps to understand the essence of stochastic population-based searches that solve ill-conditioned global optimization problems. This condition manifests itself by presence of lowlands, i.e., connected subsets of minimizers of positive …

Jakub Sawicki, Marcin Łoś, Maciej Smołka, Robert Schaefer

13.02.2021 Open Access

Actively revealing card attack on card-based protocols

In 1989, den Boer presented the first card-based protocol, called the “five-card trick,” that securely computes the AND function using a deck of physical cards via a series of actions such as shuffling and turning over cards. This protocol enables …

Ken Takashima, Daiki Miyahara, Takaaki Mizuki, Hideaki Sone


Analyzing randomness effects on the reliability of exploratory landscape analysis

The inherent difficulty of solving a continuous, static, bound-constrained and single-objective black-box optimization problem depends on the characteristics of the problem’s fitness landscape and the algorithm being used. Exploratory landscape …

Mario Andrés Muñoz, Michael Kirley, Kate Smith-Miles


Similarity in metaheuristics: a gentle step towards a comparison methodology

Metaheuristics are found to be efficient in different applications where the use of exact algorithms becomes short-handed. In the last decade, many of these algorithms have been introduced and used in a wide range of applications. Nevertheless …

Jesica de Armas, Eduardo Lalla-Ruiz, Surafel Luleseged Tilahun, Stefan Voß

03.02.2021 | Correction

Correction to: Complexity-theoretic aspects of expanding cellular automata

In the original publication, certain mathematical formulas were rendered incorrectly or are missing from the text.

Augusto Modanese

31.01.2021 Open Access

On sampling error in genetic programming

The initial population in genetic programming (GP) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to …

Dirk Schweim, David Wittenberg, Franz Rothlauf


Visualizations for rule-based machine learning

Learning Classifier Systems (LCSs) are a group of rule-based evolutionary computation techniques, which have been frequently applied to data mining tasks. The LCSs’ rules are designed to be human-readable to enable the underlying knowledge to be …

Yi Liu, Will N. Browne, Bing Xue


The influence of fitness landscape characteristics on particle swarm optimisers

In the growing field of swarm-based metaheuristics, it is widely agreed that the behaviour of an algorithm, in terms of a good balance of exploration and exploitation, plays an important part in its success. Despite this, the influence that the …

A P Engelbrecht, P Bosman, K M Malan