This work presents a novel evolutionary computation-based Padé approximation (EPA) scheme for constructing a closed-form approximate solution of a nonlinear dynamical model of Covid-19 disease with a crowding effect that is a growing trend in epidemiological modeling. In the proposed framework...

Business processes design optimization is known as the problem of creating feasible business processes while optimizing their criteria such as resource cost and execution time. In this paper, the authors propose an evolutionary multi-criteria approach based on a modified evolutionary algorithm...

For the Semantic Web point of view, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Adding logic to the Web, the means to use rules to make inferences, choose courses of action and answer questions,...

This paper describes the fuzzification and defuzzification process in the framework of hybrid systems comprising Fuzzy Cognitive Maps (FCMs) and Genetic Algorithms (GAs). More specifically, it provides a stepwise methodology for fuzzification and defuzzification aiming at both an improved...

We recently formulated an approach to representing structures in cellular automata (CA) spaces, and the rules that govern cell state changes, that is amenable to manipulation by genetic programming (GP). Using this approach, it is possible to efficiently generate self-replicating configurations...

Genetic algorithms are a class of probabilistic optimization techniques that have proved useful in a wide variety of problem domains. This paper offers an introduction and overview to genetic algorithms and examines some of the finance-related applications to which the technique has been applied.