The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean delay equations (BDEs) provide a ...

This abstract summarizes the results reported in the paper [5]. In this paper a new method of performing a local search in multiobjective optimization problems is proposed. The proposed method uses a solution acceptance criterion based on aggregation of ...

Visualising the Pareto-optimal solutions and their objectives can be challenging, more so when the number of objectives is large. The paper proposed the combined use of clustering and parallel coordinates plots to visualise the Pareto-optimal solutions. ...

Coupled models of the soil-vegetation-atmosphere systems are increasingly used to investigate interactions between the system components. Due to the different spatial and temporal scales of relevant processes and computational restrictions, the ...

The paper concerns multi-objective methodology applied to parallel Extremal Optimization (EO) used in processor load balancing in execution of distributed programs. When load imbalance is detected in executive processors then EO algorithms are used to ...

Excursions of a protein between different structures at equilibrium are key to its ability to modulate its biological function. The energy landscape, which organizes structures available to a protein by their energetics, contains all the information ...

In this work the temporal folding process with a cellular automaton like-scheme was modeled. The cellular automaton is implemented with an artificial neural network and evolved with Differential Evolution. This neural-CA model is applied sequentially to ...

The past two decades have witnessed great advances in the computational modeling and systems biology fields. Soon after the first models of metabolism were developed, methods for phenotype prediction were put forward, as well as strain optimization ...

Type 1 Diabetes Mellitus can only be treated injecting insulin and glucagon into the blood stream. This research is motivated by the challenge to accurately predict future blood glucose levels of a diabetic patient so that an automatic system could ...

Directional changes (DC) is an event based encoding for time series data that has become popular in financial analysis, particularly within the evolutionary algorithm community. In this paper, we apply DC to a medical analytics problem, using it to ...

This paper investigates the optimization of EEG signals cleaning process by elaborating a comparative study of swarm intelligence, evolutionary and memetic computation techniques. In this context, algorithms from each technique have been selected ...

The food and drinks process industry requires large volumes of hot water at varying demand temperatures. To help minimise the cost of energy usage and provide hot water at a required temperature, there has been a growing interest in the installation of ...

Human driving models aim at producing human-like driving strategies by mimicking the behavior of drivers. Drivers optimize several objectives when traveling along a route, such as the traveling time and the fuel consumption. However, these objectives ...

Booster stations are fluid systems consisting of interconnected components such as pumps, pipes, valves and fittings. One of their main applications is to supply whole buildings or higher floors with drinking water if the supply pressure of the water ...

Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. ...

Based on previous results on language games here I study cultural dynamics extended in spatial environments. The underlying model makes assumptions regarding cognitive aspects of the individuals based on the Neuronal Replicator hypothesis. Although I ...

This work presents the use of an ant colony optimization (ACO) based neuro-evolution algorithm to optimize the structure of a long short-term memory (LSTM) recurrent neural network (RNN) for the prediction of aircraft turbine engine vibrations. It ...

This paper presents the use of the ant colony optimization algorithm (ACO) for the optimization of solid waste collection in Ciudad Universitaria (CU), National Autonomous University of Mexico (UNAM). This is formulated as an Asymmetric Capacitated ...

Text summarization aims to generate condensed summary from a large set of documents on the same topic. We formulate text summarization task as a multi-objective optimization problem by defining information coverage and diversity as two conflicting ...

The cardinality-constrained portfolio optimization problem is NP-hard. Its Pareto front (or the Efficient Frontier - EF) is usually calculated by stochastic algorithms, including EAs. However, in certain cases the EF may be decomposed into a union of ...