This paper forges a strong connection between two well known computational frameworks for representing biological systems, in order to facilitate the seamless transfer of techniques between them. Boolean networks are a well established formalism ...

In this paper we present a model containing modifications to the Signal-passing Tile Assembly Model (STAM), a tile-based self-assembly model whose tiles are capable of activating and deactivating glues based on the binding of other glues. These ...

In this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient ...

Spiking neural P systems are parallel and distributed computation devices which are inspired by the neuro-physiological behavior of biological neurons. In this paper we will present, with a tutorial approach, the main underlying ideas and the most ...

Since the number of features of the dataset is much higher than the number of patterns, the higher the dimension of the data, the greater the impact on the learning algorithm. Dimension disaster has become an important problem. Feature selection ...

Food quality assessment is an important part of the food industry. The traditional food quality assessment technologies have the limitations of inconsistent and different technical defects for each method. Data mining technology has significant ...

Face recognition technology has been widely used in the field of artificial intelligence. The technology needs to be carried out normally under the appropriate light, however, there is not ideal light, even poor-lighted for the face recognition ...

The model of Spiking Neural P systems (SNP systems) is a widespread computational model in the area of membrane computing. It has numerous applications, especially related to machine learning. Most of these applications require a custom variant of ...

As one of the most widely studied membrane systems, a spiking neural P system consists of three fundamental elements: initial spikes, evolution rules and connection between neurons. The automatic design of an arithmetic operation spiking neural P ...

We consider spiking neural P systems with astrocytes producing calcium that differs from the standard spiking neural P systems in several ways: we have a new type of resources called calcium unit alongside the standard spike, we have a new type of ...

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 ...

Metaheuristics provide the means to approximately solve complex optimisation problems when exact optimisers cannot be utilised. This led to an explosion in the number of novel metaheuristics, most of them metaphor-based, using nature as a source ...

A morphogenetic system (M system) is an abstract computational model inspired by characteristic properties of morphogenetic phenomena such as controlled growth, self-reproduction, homeostasis and self-healing in living systems. Besides selected ...

This paper introduces Multi-Region Symbolic Regression (MR-SR), a general framework that divides the original input data space of symbolic regression problems into subspaces (regions), generates different solutions to fit these regions and then ...

Many complex systems observed in nature and society can be described in terms of network. A salient feature of networks is the presence of community patterns. Network-based models have already been applied in the analysis of data from very diverse ...

It is well known that machine learning (ML) techniques have been playing an important role in several real world applications. However, one of the main challenges is the selection of the most accurate technique to be used in a specific ...${}_{\mathrm{}}$

Ensemble learning is a system that combines a set of base learners to improve the performance in machine learning, where accuracy and diversity of base learners are two important factors. However, these two factors are usually contradictory. To ...

Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are ...

Koniocortex-Like Network (KLN) model is a Bio-Inspired Neural Network structure that tries to replicate the architecture and properties of the biological koniocortex section of the brain. Based on its biological counterpart that behaves as a Winner-Take-...

As neuroscience gradually uncovers how the brain represents and computes with high-level spatial information, the endeavor of constructing biologically-inspired robot controllers using these spatial representations has become viable. Grid cells are ...